METHOD AND SYSTEM FOR COMPARISON OF 3D MODELS

Description

[0001] The present application claims priority from U.S. Provisional Patent Application 61 / 916,279, filed on December 15, 2013. technical field

[0002] This patent application relates to the field of 3D models, for example digital representations in a 3-dimensional space, and more specifically to the field of comparing 3D models, particularly through descriptors, for the purposes of searching, classifying and / or comparatively analyzing parts. Previous technique

[0003] Three-dimensional digital representations of objects are commonly used in the fields of engineering, medicine, video games, film, and even mass-market applications (such as SketchUp by Google, now Trimble). This use is made possible in particular by 3D creation and editing software, including CAD systems ( computer-aided design), reverse engineering, and other 3D reconstruction techniques, as well as 3D scanning equipment, all generate three-dimensional digital models. Some of these models are 3D models that describe the boundaries between objects and their environment, and are called representations by borders (in English boundary representation Or B-rep, which can be found, for example, in the STEP standard or in geometric modelers such as Parasolid or Acis). Others go further with a facetization or a tessellation (such as STL or VRML formats, for example). These models can include information other than purely geometric information.

[0004] The aforementioned types of 3D models are widespread in businesses and many sectors of daily life, and their already large number is rapidly increasing. Consequently, a combined problem arises: (i) cataloging 3D models, (ii) finding similar digital objects, (iii) comparing them to identify differences or identical parts, and finally (iv) displaying the relevant results to ensure simple and efficient use based on defined criteria.

[0005] Today there are two distinct categories of systems: one to compare a reference 3D model to a second 3D model to determine the differences between them, and the other to search for 3D models that resemble a given reference 3D model. 3D model comparison systems or tools

[0006] Several computer applications, including CAD systems, offer methods for accurately comparing two 3D models, a 3D model with a point cloud, or a 3D model reconstructed from a real object. In this approach, two 3D models are first selected. Several different methods can be chosen to compare these two models. One can use topological and geometric structure comparison with graph-type approaches (Graph mapping, such as WO 2007 / 098929 A1), or a point-based method (such as US 7,149,677 B2, or the least squares method, as in the Innovmetric product or US 8,072,450 B2). Some methods require that the two models be in the same reference frame, in the same computer application (the same CAD system, for example), and use the same geometric representation.Methods based on comparison at the geometric and topological levels absolutely require that the two 3D models being compared meet these conditions. Depending on the comparison method used, the marking of the 3D models distinguishes the identical and different parts of the two 3D models by associating them, for example, with a state (identical, modified, unique) or by quantifying the differences between the two 3D models (a distance between points in models A and B, respectively, for example). The result of the comparison of the two 3D models is then displayed, either (i) by superimposing the two 3D models, (ii) by applying a color gradient to highlight the difference between models A and B or vector fields, or (iii) a color code based on the state defined in the previous module.This comparison identifies identical parts of the 3D model (the faces), unique parts (unmatched faces), modified parts (matched faces with a difference), and possibly parts added to one model compared to the other (hence the symmetrical display to see what exists on A compared to B and what exists on B compared to A). Although all these systems are different, they all share one common point: when comparing the reference 3D model with the target 3D model, they choose a single reference frame or a single "best fit" from which they will compare the complete 3D models to show the differences.

[0007] The Geometric Ltd application included in SolidWorks is a typical and representative example of this category. 3D model search systems or tools

[0008] A second category of applications involves searching for 3D models by comparing a reference 3D model with a set of other 3D models. The goal is to select models based on their similarity to the reference model. This similarity is usually expressed as a quantitative value used to rank the 3D models. Various methods exist for determining this similarity. Initially, each 3D model is analyzed to produce a more efficient representation for subsequent comparisons. This representation is often referred to by various terms, including descriptor, representation, and index. Regardless of its name, this representation can take several forms, such as a graph (US 2004 / 0249809 A1), a vector of fixed dimension N (US 6,625,607 B1), or a vector of variable dimension N depending on the 3D model, and so on.These descriptors are generally saved for use when comparisons are performed. A 3D model is chosen as a reference. In some cases, the reference 3D model is replaced by two-dimensional images, photographs, or drawings [A 3D Model Search Engine, Patrick Min, Ph.D. Dissertation, Princeton University, 2004]. If necessary, its descriptor is calculated. The descriptors are then compared. Here again, several methods are described in [Content-based Three-dimensional Engineering Shape Search, K. Lou, S. Prabhakar, K. Ramani, Proceedings of the 20th International Conference on Data Engineering 2004]. Based on the comparison results, similar 3D models are usually ranked according to a quantity, which we call the similarity index. These results are then displayed in various forms, usually small images (icons, thumbnails).US patent 6625607 B1 and the article "Fast Content-Based Search of VRML Models Based on Shape Descriptors" by KOLONIAS I ET AL, ISSN: 1520-9210, DOI: 10.1109 / TMM.2004.840605 disclose two other methods known from the prior art.

[0009] As mentioned previously, numerous approaches have been proposed for searching for 3D models relative to the shape of a reference model. Some approaches target organic shapes, while others aim for permissive similarity (e.g., finding cars, chairs, or glasses). In particular, these methods are not designed to determine whether two 3D models are exactly the same, with accuracies on the same order of magnitude as manufacturing tolerances. Systems such as Siemens' Geolus or Cadenas' GeoSearch are representative of this category. They analyze 3D models approximated by planar faceted representations to generate their descriptors, thus inevitably degrading their accuracy.Several proposed methods for extracting descriptors from 3D models and for comparing these descriptors with each other require high computation time and are therefore difficult to apply when the number of 3D models to be processed is large (more than one million models, for example) or for interactive searching.

[0010] However, known 3D model search systems or tools do not possess the information or technologies to offer the ability to launch a search based on the consideration of complete 3D models that prioritize parts of these 3D models. Summary of the invention

[0011] There is a need for systems that can find 3D digital models from heterogeneous sources that are similar in whole or in part, that determine and qualify the differences from a reference 3D model considered in whole or in part, that display the results with all relevant information on the differences and similarities, all in an extremely precise manner according to different criteria and that are suitable for handling very large quantities of 3D models.

[0012] To address these needs, we propose a process that first constructs descriptors to identify (or index) 3D models. This descriptor is distinguished by its ability to capture properties at the level of a complete 3D model, of each solid body (in the usual sense of three-dimensional geometric and topological representation) that may compose a 3D model, and also properties at the level of each face (again, in the usual sense of three-dimensional geometric and topological representation) that composes the 3D model. Boundary representations do not guarantee the uniqueness of a 3D model; that is, several boundary representations could correspond to the same object.To obtain this property, essential for comparing 3D models, we group together faces that are continuous G2 (geometric second derivative) along one of their common edges, and we split along the discontinuity the faces constructed on a surface that is not continuous G2. All subsequent processing, except for 3D display, is possible on the descriptors without necessarily having to use the 3D models.

[0013] In the next stage of the proposed process, the characteristics of the descriptors are compared according to criteria selected by the operator. This phase relies on two essential elements of the invention. One of these is the reconciliation of the terms or characteristics of the descriptors, which corresponds to a pairing of solid bodies and faces of 3D models. These models can therefore be expressed in different reference frames, different CAD systems, and different geometric and topological representations, thus allowing only a portion of the 3D models to be considered for processing. The other element is the more rigorous qualification of differences at the face level, compared to current methods which are limited to identical, modified, or new. We introduce up to nine qualities (identical, intrinsically identical, identical geometric type, different topology, etc.); a face can have several qualities simultaneously.The display marking relies on these properties (for example, a color associated with a combination of qualities) and the operator's query criteria. The aggregation of all the differences determines the resemblance (or similarity) index, which ranks the 3D models.

[0014] Since the digital objects being manipulated are 3D models, we propose a new way of communicating the results as a 3D structure within a single 3D space. One axis represents the similarity index, the colors of the faces are used to mark the types of differences, and the two remaining axes are used, for example, to represent other quantities or properties such as costs, dates, suppliers, configurations, versions, etc.

[0015] This process is used, for example, (i) to identify 3D models identical to a reference 3D model with a precision comparable to that of their manufacture (identifying duplicates or geometric clones); (ii) to identify an identical or similar part (solid body) in one or more mechanical systems (a 3D model); (iii) to identify an identical or similar component (set of solid bodies) in one or more systems (3D models); (iv) to identify similar 3D models in large, possibly distributed, model databases; etc.

[0016] Thanks to the descriptors that capture properties with very fine granularity (set of parameters or characteristics at the level of each face) and the comparator's capabilities, including the reconciliation principle and face qualification, the system also works at the level of identifying form features, i.e. parts of 3D models such as for example to identify (i) a set of through holes of given diameter and center distances (set of disjoint faces and their parameters) in various 3D models; (ii) a keyway (set of adjacent faces and intrinsic parameters); (iii) to identify a face or a set of identical or similar faces in one or more 3D models; etc.

[0017] Each part, namely (i) the method for searching for similar 3D models based on volumetric and boundary descriptors, (ii) the reconciliation of terms or features of the descriptors allowing for multiple repositioning and the identification of sub-parts of the 3D model (shape features), (iii) the principle for qualifying differences, and (iv) the display of results combining similarities and differences between several 3D models in a single 3D space, are relevant aspects of the invention. The invention is defined by independent claims 1 and 10. The dependent claims define additional embodiments.

[0018] In one aspect, a method for comparing 3D models is proposed for the purpose of searching, classifying, and / or comparatively analyzing representations by the boundaries of 3D models. According to this method, a descriptor is assigned to each of a plurality of 3D models. These descriptors include geometric features of the 3D models that are invariant with respect to the modeling coordinate system and geometric features of the 3D models that are coordinate system dependent. Coordinate system invariant features are used to match parts of a first 3D model with parts of at least one other 3D model. Coordinate system dependent features are used for at least the parts that are successfully matched to determine a set of transformations associating parts between the first 3D model and parts of at least one other 3D model.At least one group of identical transformations is determined, where the similarities and differences between 3D models are identified, at least in part, by said group of assimilable transformations.

[0019] Geometric features of 3D model parts that are invariant with respect to the 3D model frame may include the area, eigenvalues ​​of the moments of inertia and / or the perimeter of the 3D model planar faces, and geometric features of 3D model parts that depend on the 3D model frame include the center of gravity and / or the axes of the moments of inertia of the planar faces.

[0020] The step of determining at least one group of identical transformations may include determining at least two groups of identical transformations in order to allow comparison of similar 3D models but where at least part of the 3D models is in a different position and / or orientation.

[0021] A second aspect proposes a method for comparing 3D models for the purpose of searching, classifying, and / or comparatively analyzing the boundary representations of 3D models. According to this method, a descriptor is assigned to each of a plurality of 3D models. These descriptors include geometric features of the 3D models that are invariant with respect to the modeling coordinate system and geometric features of the 3D models that are coordinate system dependent. Features that are invariant with respect to the coordinate system are used to match the faces of a first 3D model with at least one other 3D model. Data is stored, classified according to the determined similarities and differences between the features associated with the descriptors of said at least one other 3D model.For at least the faces whose matching is successful, a transformation is determined associating the faces between the reference frames of the first 3D model. And one or more of said at least another 3D model is rendered whose characteristics are coded by color or texture according to the data representing the similarities and the properties of the differences.

[0022] Geometric features of 3D model elements that are invariant with respect to the modeling frame may include the area, eigenvalues ​​of the moments of inertia and / or the perimeter of the planar faces of the 3D model, and geometric features of 3D model elements that depend on the modeling frame include the center of gravity and / or the axes of the moments of inertia of the planar faces.

[0023] A third aspect proposes a method for comparing 3D models for the purpose of searching, ranking, and / or comparatively analyzing the boundary representations of the 3D models. According to this method, a descriptor is assigned to each of a plurality of 3D models. These descriptors include geometric features of the 3D models that are invariant with respect to the modeling coordinate system and geometric features of the models that are coordinate system dependent. Features that are invariant with respect to the coordinate system are used to match the faces of a first 3D model with at least one other 3D model. The initial data collected for this matching is stored for at least some other 3D models. A first result for searching, ranking, and / or comparing the boundary representations of the 3D models is provided, using at least some of the initial matching data.We continue to use features that are invariant with respect to the coordinate system to match faces between the first 3D model and at least one other 3D model, building upon this initial matching data. We then provide a second result for searching, ranking, and / or comparing representations using boundaries, at least partially utilizing the refined matching data.

[0024] A fourth aspect proposes a method for comparing 3D models for the purpose of searching, classifying, and / or comparatively analyzing representations by the boundaries of 3D models. According to this method, a descriptor is assigned to each of a plurality of 3D models, the descriptors comprising characteristics of the 3D models. Increased or decreased importance is assigned to one or more characteristics in a first 3D model. A matching value is generated between a first 3D model and at least one other 3D model using the characteristics and, where applicable, the importance assigned to one or more of these characteristics of the first 3D model.

[0025] A fifth aspect proposes a method for comparing 3D models for the purpose of searching, classifying, and / or comparatively analyzing representations by the boundaries of 3D models. According to this method, a descriptor is assigned to each of a plurality of 3D models. A matching value is generated between a first 3D model and several other 3D models using their respective descriptors. A display of several 3D models is then generated, arranged spatially in groupings, where each grouping is defined according to a common descriptor characteristic. The groupings can be based on a search criterion, the importance given to one or more descriptor characteristics, or a search weighting.

[0026] According to a sixth aspect, a method for comparing 3D models is proposed for the purpose of searching, classifying and / or comparatively analyzing representations by the boundaries of 3D models. According to this method, a descriptor is provided to each of a plurality of 3D models, taking into account the geometrically continuous faces of order 2 commonly called "G2", preferably by unifying them and cutting out the faces whose surfaces are not geometrically continuous of order 2 (G2 surface).

[0027] A seventh aspect proposes a method for comparing 3D models for the purpose of searching, classifying, and / or comparatively analyzing representations by the boundaries of the 3D models. According to this method, at least two corresponding parts are selected from two 3D models. The parts that are similar to each other are identified, and the parts that are different are categorized according to one or more types of differences. At least one of the 3D models is displayed, marking the identical and different parts according to the categorization.

[0028] According to some embodiments, a 3D model search system for locating 3D models from a collection of 3D models corresponding to a search query 3D model by means of 3D model descriptors includes a feature selector for selecting at least one feature or part of the search query 3D model to which a different weighting is assigned for the purposes of the search, and a 3D model comparator, said comparator configured to compare a descriptor of said search query 3D model to the descriptors of said collection of 3D models taking into account said weighting of said feature or part of said search query 3D model, and to produce a list of matching models according to the comparison.

[0029] The feature selector can assign weighting values ​​to the parameters of a descriptor of the search query 3D model. The system may further include a search results binder and an image generator configured to generate a displayable image of at least a portion of the list of matching 3D models, arranged and organized according to preselected criteria. These matching 3D models may be arranged according to a similarity index calculated from the descriptors. These preselected criteria may include a criterion that depends on the descriptor, characterized in that the matching 3D models are ranked according to characteristics that depend on the descriptor. The feature selector may be configured to recognize the characterizing aspects of the descriptors in the results binder and to select one of these characterizing aspects.The descriptors can include a volumetric component and a face and / or an edge component.

[0030] According to some embodiments, a 3D model search system for identifying 3D models in a collection of 3D models corresponding to a search query 3D model from 3D model descriptors includes a selector for selecting a 3D query model, a 3D model comparator configured to compare the query 3D model descriptor with descriptors from the 3D model collection and provide a list of matching 3D models, and a search results sorter and image generator configured to generate a displayable image of at least a portion of the list of matching 3D models, arranged and organized according to preselected criteria.

[0031] The corresponding 3D models can be arranged according to a similarity index calculated from the descriptors. The preselected criteria may include a criterion that depends on the descriptor, characterized in that the corresponding 3D models are ranked according to characteristics that depend on the descriptor. The generated image may display models organized into several columns or rows according to their similar characteristics.

[0032] According to some embodiments, the manufacturing process of a product is characterized by the use of a 3D model for the manufacture of at least one part of the product, said 3D model being directly selected and possibly modified using a 3D model search / comparison system or the process according to one or the other of the embodiments according to the present invention.

[0033] The reader should understand that the present description may set forth several inventions, and the reference in this application to invention may mean any one of these inventions. Definitions

[0034] In this patent application, the following terms shall have the following meanings: 3D model

[0035] In this text, we use the term 3D model to refer to a digital representation of an object in a 3-dimensional space. A 3D model describes the boundaries between one or more objects and their environment. 3D models are commonly based on boundary representations (B-rep), as in the STEP standard or the geometric modelers of CAD / CAM systems, for example, or on tessellation, as in STL or VRML formats, for example. A boundary is composed of one or more faces, which are surfaces bounded by one or more contours. A face, a plurality of faces (contiguous or not), or a set of faces representing a closed 3-dimensional Euclidean subspace (generally called a solid body) are all examples of 3D models. A set of faces representing a plurality of solid bodies is also a 3D model.In this definition, any part of a 3D model is a 3D model, and any composition of 3D models is a 3D model. 3D models can include information other than purely geometric details, such as the properties of the materials used or the recommended manufacturing techniques. descriptor

[0036] A representation of a 3D model adapted for a specific use, normally expressed more efficiently and concisely than the 3D model itself. It is a set of information that characterizes a 3D model or a part thereof. In some cases, the descriptor may include a list of volumetric geometry characteristics of a 3D model, including the dimensions of the 3D model's bounding box, its volume, its area, its principal moments of inertia, its center of mass, its principal axes of inertia, and a list of solid bodies of the 3D model, including for each of them—and which also forms a 3D model—their volume, total area, principal moments of inertia, center of mass, and principal axes of inertia.

[0037] In some cases, a descriptor may include, for example, a list of faces of the 3D model and, for each face, a list of characteristics such as the type of the underlying surface and its parameters, the dimensions of the bounding box, the area, the perimeter, the principal moments of inertia, the centroid, and the principal axes of inertia. The differences and similarities between 3D models are obtained through a process called descriptor comparison. In its simplest and most basic form, a 3D model can be a descriptor in its own right. Similarly, a 3D model of a more complex object can itself have several different descriptors, either by being composed of several descriptors (each of which could be considered a constituent 3D model), or by taking into account only certain information and not other characteristics of the object itself. shape characteristics

[0038] A part or property (or attribute) of a descriptor (and therefore of a 3D model) that is of particular interest for a given process. An element of the descriptor, or any combination of multiple elements of the descriptor, defines a shape feature. For example, the diameter of a cylindrical face is an elementary shape feature, the center-to-center distance between two holes is a shape feature, and a round-ended keyway is a shape feature. Each of these examples corresponds to a part of a descriptor, or the shape feature can be determined from the descriptors. In its most basic form, a shape feature is simply called a characteristic.A feature is said to be intrinsic to a 3D model or its descriptor if it depends only on that 3D model or its descriptor and can be determined from it, and not from the context—that is, a 3D model that encompasses the feature. Otherwise, the feature is said to be extrinsic. matching

[0039] The process of determining the correspondence between the characteristics of two descriptors. A portion of a descriptor could, for example, characterize a solid body or a face. Matching consists of associating this portion of the descriptor with another portion of a descriptor (the same or a different descriptor) when the two descriptor portions share common characteristics (identical or within ranges of values, for example). This process allows, among other things, the matching of solid bodies or faces of 3D models via their respective descriptors, regardless of their position in space. This matching process does not require that the 3D models be described with the same geometric representation (STEP and Parasolid, for example) nor that they be expressed in a common reference frame. reconciliation

[0040] The process of grouping descriptor parts by considering the relative position in space of the shape features associated with these paired descriptor parts. The process consists of determining the groups of transformation matrices that allow shape features to be placed in a common coordinate system. This process allows, among other things, the recognition of identical or similar shape features between 3D models. Thus, at the face level, for example, the matching identifies faces that share intrinsic characteristics, such as being cylindrical, having a diameter between 5 and 6 mm, and a length of 20 mm. During reconciliation, extrinsic parameters are taken into account, such as the direction of the axis of cylindrical faces being equal to the Z-axis and the center-to-center spacing being 20 mm. This matching-reconciliation process can be applied iteratively. comparative analysis of descriptors

[0041] A process that allows for the search, determination, and qualification of identical and different characteristics between descriptors according to the query specified by the operator. The comparison is performed on the entire descriptor or only on parts of it, and a different weighting, specified by the operator, can be assigned to each part to emphasize certain shape characteristics or even to consider only a portion of the 3D model. The resemblance index (or similarity index) is determined from the comparative analysis of the descriptors. operator

[0042] Refers to both a human operator and another computer system, interacting with the system, in interactive mode or offline (batch mode). Brief description of the drawings

[0043] The embodiments of the invention will be better understood through the drawings, which are shown below and which accompany many examples appearing in the detailed description of the embodiments of the invention, including: There figure 1 presents a diagram of a sequence of steps in a process according to an embodiment of the invention; The figure 2 presents a diagram of a sequence of steps of a process according to another embodiment of the invention; The figure 3 presents a block diagram of a computer system according to an embodiment configured to execute at least the process according to the figure 1 ; There figure 4 is an orthogonal projection of a 3D model; The figure 5 is an orthogonal projection of a simplified version of the 3D model of the figure 4 ; There figure 6 is an orthogonal projection of the 3D model of the figure 5 which generically identifies the faces of 3D models; The figure 7Ais an orthogonal projection of the 3D model used as the reference 3D model in the description of this implementation; The figure 7B is an orthogonal projection of a 3D model similar to that of the figure 7A used to describe the comparison process; The figure 8A is an orthogonal projection of the 3D models of Figures 7A and 7B following an initial positioning; The figure 8B is an orthogonal projection of the 3D models of Figures 7A and 7B following a second positioning; The figure 9 illustrates an orthogonal projection of the reference 3D model and an interface allowing the operator to define constraints, showing a set of constraints used to describe a particular realization; The Figure 10 is an orthogonal projection of a plurality of similar 3D models positioned and marked according to the result of their comparison with the reference 3D model and the constraints defined by the operator; The figure 11presents a block diagram of a version of the computer system of the figure 3 following a distributed architecture. Detailed description of the achievements of the invention

[0044] The achievements of the invention are described according to the Figures 1 And 2 These steps show the sequence of steps to be taken to simultaneously determine the similarities and differences between a reference 3D model and a number of other 3D models based on their descriptors. The number of 3D models considered can vary enormously. If the goal is to compare a reference 3D model to a few known 3D models, this number can be small. However, if the goal is to find a suitable or similar model, one might search among thousands or even millions of existing 3D model descriptors. Figure 3essentially illustrates a functional diagram of a software system which can perform the steps illustrated in Figures 1 And 2 .

[0045] There Figure 4 illustrates an example of a 3D model of a part. To simplify the description of the following models, the Figure 5 shows a simplified version of the 3D model of the Figure 4 and this simplified version will be used in the following description. The illustrated part contains two circular openings of different dimensions in its upper and lower parts and a middle part which connects the upper and lower portions, the aforementioned lower part being in the form of a flat base.

[0046] In the figure 6 The various faces are marked from Si,1 to Si,20. The labeling is entirely arbitrary and may differ from one 3D model to another. Thus, the labeling Sm,fof a face identifies face f of the 3D model m. The descriptor in this implementation could consist of a list of characteristics, such as the type of the 3D model ( Kind ), the parameters which depend on the type ( TypePar ), the volume, in the case of a solid body, or the area, in the case of a face ( Int(N) ) , the area for a solid body or the perimeter for a face ( Int(N-1) ) , the center of gravity ( Gx,y,z ) , moments of inertia ( M1,2,3 ) , the axes of inertia ( Ai x,y,z with i=1,2,3 ) and the dimensions of the enclosing box ( Bx,y,z ) for each of the solid bodies composing the 3D model and for the complete 3D model, as well as for each of the faces.

[0047] Boundary representation does not respect the uniqueness property; that is, more than one boundary representation can exist for a given 3D model. This uniqueness property is essential for comparing 3D models. For example, when using different modeling systems (say, two CAD systems), the face division of a 3D model's boundary may not always be unique. To address this, we use "Strictly Continuous G2" faces. Thus, faces can be grouped into a single face if the geometric second derivative, usually denoted G2, is continuous along an edge common to two faces. Similarly, a face whose underlying surface is not G2 continuous would be split into pieces such that the underlying surface of each face is G2 continuous.By proceeding in this way, the uniqueness property is restored at the level of the faces of the representation by the boundaries.

[0048] Table 1 gives the characteristic values ​​for the 3D model of the figure 7a The link between the identifier of a 3D model in the descriptor (column Mod3D ) and the 3D model itself is created using a lookup table, for example.

[0049] There figure 7.b presents another example of 3D models. Table 2 gives the values ​​of the characteristics of its descriptors.

[0050] In the Figure 1 The descriptor generation step is S100. All 3D models undergo this step, which can nevertheless be performed at different times. Suitable descriptors can be stored with the 3D model data or separately.

[0051] To the figure 33D models are stored in an S30 data store. This can be a single data store or a distributed storage of 3D models. An S32 descriptor generator reads the 3D models from the S30 data store and saves the descriptors to a second S34 data store. This S34 data store can be integrated with the S30 data store or located separately, even remotely.

[0052] The system may include a 3D model management system (S40) which controls which 3D models will be compared. A reference 3D model selector or generator (S42) allows an operator to select or create a reference 3D model. The descriptor of the reference 3D model can be generated using the descriptor generator (S32) and fed into the search or comparison engine (S50). The S50 engine retrieves the descriptors from the data store (S34).

[0053] The system of the Figure 3can reside on a standalone workstation or be distributed across different computers and / or servers to facilitate data processing, storage, and management.

[0054] There figure 8A shows the 3D model of the Figure 5 on which is superimposed a second 3D model, namely that of the figure 7B The latter presents differences compared to the reference 3D model of the figure 7A Namely, the middle section is longer (the center-to-center distance has increased from 120mm to 150mm), and the middle section connects to the lower section at a different angle of 30 degrees. The 3D model of the figure 7A is the reference 3D model selected in step S102 of the figure 1 , and the 3D model of the figure 7B is the one to which the reference is compared to explain the method. It is understood that this comparison is performed for each available 3D model.

[0055] There figure 8BThe two 3D models are presented in different relative positions. Systems described in the prior art use only a single relative position to compare 3D models. This position is determined either automatically from the reference frames specific to each 3D model, by a "best-fit" of the two 3D models, or by an operator. The approach adopted in the following implementations determines, from the face characteristics of the descriptors, all the relative positions that allow for the matching of shape characteristics (sub-parts of the initial 3D models) from the compared 3D models.

[0056] While a solid-body comparison between two 3D models can yield useful results for research purposes, it is not a practical way to locate a 3D model within another (more complex) 3D model that might contain one or more parts of the desired 3D model. A face comparison allows for matching faces to identify 3D models that could potentially contain the desired 3D model.This concordance of faces is obtained by an iterative process of comparing descriptors in two steps, namely a matching step based on the intrinsic characteristics of the 3D model, for example its area or its moments of inertia, followed by a reconciliation step based on the extrinsic characteristics of the 3D model, for example its center of gravity or its axes of inertia, these characteristics depending on the 3D model but also on the frame of reference in which it is described and more generally on its context.

[0057] The identification (in the labeling sense) of faces will typically differ between compared 3D models; however, in our simplified example, the numbering is identical solely to facilitate understanding of the process. Using intrinsic face characteristics from the descriptor, faces among the 3D models are matched. For example, the area characteristic values Int(N),perimeter Int(N-1), and the three moments of inertia M1,2,3Faces Si,5, Si,18, Si,6, and Si,4 are unique and identical, and therefore these faces are matched between the two 3D models considered with a maximum confidence level of 1. However, the intrinsic values ​​for faces Si,7, Si,8, Si,16, and Si,17 are not unique and result in multiple potential face matchings, for example, S1,7 with {S2,7; 52,8; S2,16; S2;17} or S1,9 with {S2,9; S2,25}. The indeterminacy will be resolved during the reconciliation step. For faces S1,1, S1,2, and S1,3 with 52,1, S2,2, and S2,3 respectively, the intrinsic feature values ​​are unique, but some values ​​differ between the faces of the two compared 3D models. These faces are matched, but the confidence level is less than 1.The method for calculating this index can vary; we propose here a simple method to facilitate understanding: summing the index assigned to each considered characteristic and dividing it by the number of characteristics considered. For a quantitative parameter, for example, the index is equal to one minus the absolute value of the ratio between the difference in values ​​for that characteristic and their mean, i.e., |(Vref-Vcomp / ((Vref+Vcomp) / 2)|. For a qualitative parameter, a table of values ​​corresponding to the cases of difference is used. This method is given as an example and is defined differently in other implementations of the process. This index contributes to determining the similarity index between the 3D models in our implementation. In the general case, the number of faces of the two compared 3D models is different, and at the end of this step, some faces may not be matched.

[0058] The result of the comparison of the descriptors for our simplified example following the realization described after the matching step is given in Table 3.

[0059] This table shows a comparison between intrinsic features of solid bodies, revealing an imperfect match (similar but not identical 3D models), followed by a comparison between intrinsic features of faces. Several cases are illustrated: perfect unique matches, multiple matches with identical feature values, and matches based on only partially identical feature values, resulting in a confidence level less than 1. Ambiguities are resolved in a subsequent reconciliation phase.

[0060] The matching process has been described at the level of the faces of a 3D model. It is understood that a similar process is applicable at the level of a plurality of solid bodies, if they exist in the 3D models. The matching is performed using their intrinsic characteristics, such as volume ( Int(N) ), the area ( Int(N-1) ), the three Moments of Inertia ( M1,2,3 ) with our example descriptor following the same principle. The process is therefore also used to compare assemblies of 3D models, for example, or to find subsystems in more complex systems. From this step (step S120 of the figure 1The confidence index corresponds to a temporary similarity index used to increase computational efficiency in the process. When a 3D model has a confidence index relative to the reference 3D model that falls below a threshold determined by the operator, it is not necessary to further compare the descriptors and the temporary similarity index (step S122 in the Figure 1 ) provides a quick indication of potentially similar 3D models and thus allows large numbers of 3D models to be processed without refining the comparison when not required.

[0061] Matching compares 3D models or parts thereof, which in our definition are also 3D models, whether a face, a set of faces (shape features), or one or more solid bodies, by analyzing intrinsic parameters of the descriptor. At this stage, each 3D model is considered in isolation, without taking into account its relative position with respect to other 3D models.

[0062] Although the scale of a 3D model can generally be considered well-defined, and a low confidence index on dimensional features can be a reason to reject a match, it is understood that in some cases a scale factor can be determined during the face matching process, and that the match can thus be achieved, for a given scale factor.

[0063] Adding data to the table rows for each 3D model that is compared to the reference 3D model constitutes step S124 of the figure 1 When a sufficient number of similar 3D models are reached, or when all available descriptors have been compared, the comparison refinement process continues in step S126. From then on, the most similar 3D models can be identified based, for example, on the number of matched faces, the ratio of the total area of ​​the matched faces to the sum of the areas of all faces, or any other function that would determine a temporary similarity index. In the figure 1 The process continues with the reconciliation stage of the 3D models, which, according to our definition, are one or more faces or one or more solid bodies. It could then focus on the most promising 3D models.

[0064] Regarding the Figure 3The S52 comparison engine performs the comparison of the descriptors, and the comparison data presented in the tables is stored in the S54 data store. The S50 engine controls the process and performs the calculation of the similarity index.

[0065] The S140 reconciliation step of the figure 1This involves taking into account the extrinsic characteristics of the descriptors, particularly the relative positions of the 3D models. From the confirmed matches (confidence index = 1), transformation matrices are determined, which reposition the compared 3D models in a common coordinate system, for example, the coordinate system of the reference 3D model. Determining these matrices is a fairly routine and well-known operation in the prior art. At this stage, several transformation matrices are created. Applying them to the 3D models reveals that some of them only reconcile the 3D models used to determine them. Their relevance is low, and they are eliminated in step S142.

[0066] It is understood that one can calculate the transformations or coordinate transformation matrices between all the geometric features of the descriptors (parts of the 3D models, such as faces) and then find at least one group of identical transformations (taking into account calculation tolerances), just as one can calculate a transformation for a first feature (e.g., a first face) and then attempt to apply this transformation to other features. In this way, one can develop a group of features sharing a common transformation.

[0067] For each repositioning obtained by applying the selected coordinate transformation matrices, the comparison of extrinsic characteristics is performed (step 144). In our example, two matrices are thus selected. A first matrix, M1, positions the 3D models as illustrated by the figure 8AThe faces Si,5, Si,6, and Si,18, which were already matched with certainty, are reconciled and agree; they are declared identical for this particular positioning. By applying this matrix, the ambiguities in the matching of faces Si,7, Si,8, Si,9, Si,15, Si,16, and Si,17 are resolved. These faces are reconciled and are also declared identical for this positioning. Finally, by comparing the extrinsic characteristics of the descriptors of faces S1,2 and S1,10 with S2,10 and S2,14, we observe that several values ​​are identical and others are different. These faces are reconciled with a confidence level of less than 1. They are different, and these differences can be qualified from the comparison of the descriptors, as we will show in the marking step. The application of the second matrix M2 positions the 3D models as represented by the figure 8BThe faces Si,4 and Si,6, which are paired with certainty, are reconciled, agree, and are therefore declared identical for this second positioning. Finally, the faces Si,11, Si,12, and Si,13 are reconciled with an index less than 1, and therefore with differences for this particular positioning. Depending on the threshold set for the confidence index, a face such as Si,3 is also reconciled. It is also possible that several change-of-reference matrices lead to reconciling faces multiple times. In this case, the reconciliation giving the best confidence index is retained. These matrices do not resolve all ambiguities in all cases, and some faces remain orphaned. These are new faces or faces that have been significantly modified.

[0068] Table 4, supplemented by the data added during the reconciliation step, is shown for our example. This table can be stored with all the data retained, or only the data relevant to the subsequent steps in the process and the operator's needs.

[0069] It becomes clear that the multi-positioning principle introduced in this invention completely redefines the notion of comparison and difference between 3D models and allows for the comparison of parts of 3D models (form features). Thus, comparison can be performed using all positioning matrices, by favoring one or more specific matrices, or even by constraining the determination of a matrix based on certain characteristics of the descriptors selected by the operator. It is understood that in step S146, it is possible to calculate similarity indices that depend on these choices.

[0070] We can now truncate the comparison data table to remove multiple and ambiguous face matchings from the matching step due to their intrinsic feature values, and whose irrelevance is shown during reconciliation (step S148). Table 5 illustrates the comparison data after cleaning for our example.

[0071] The comparison refinement process continues until a sufficient number of similar 3D models are reached or all available descriptors have been compared, at step S150.

[0072] It is understood that such a process of comparison between 3D models is useful for many applications such as finding similar 3D models, classifying them according to a similarity index, classifying (clustering) these 3D models, highlighting identical or different characteristics, or enabling identification and reconciliation of references when a 3D model is substituted for a reference 3D model.

[0073] The S56 reconciliation engine (see Figure 3 ) performs the aforementioned operations and updates the data in the S54 data store.

[0074] With reference to the Figure 3The 3D models most similar to the reference 3D model, identified as such by the S50 engine (which uses data from the array in the S54 data store, fed by the S52 and S56 engines), are now ready to be presented to the operator via the S46 display system. The S46 system generates the desired view of the 3D models. To do this, it accesses the 3D models stored in the S30 data store, and the S45 marking engine uses comparison data from the S54 data store to mark identical or different faces, or other characteristics of the 3D models. The operator selects the desired marking parameters or criteria via the S44 interface, which also allows the selection of properties and functions that determine the arrangement of the 3D models in the 3D space generated by the S46 system.In some applications, S46 uses a CAD system or a web browser to display the results in 3D. The layout and marking of the displayed 3D models vary according to the operator's needs. For example, in some cases, the operator may want to highlight identical parts of the 3D models for a particular positioning, for multiple positionings, or to highlight differences in one or more characteristics. This method of communicating the simultaneous comparison of several 3D models is based on the marking and positioning of the 3D models in a 3D scene, as illustrated in Figure 1. Figure 10 is completely new.

[0075] It is understood that when 3D models are compared in the proposed process, several types of differences between faces, sets of faces (form features), and solid bodies are determined. The marking distinguishes them according to the queries formulated by the operator. Here are some examples illustrating the types of differences based on the operator's needs. In the first example, the operator searches a set of 3D models for form features identical to those present in the reference 3D model, without specifying them. In this case, the process implements matching, reconciliation, multi-repositioning, and marks the faces accordingly.The faces Si,4; Si,5; Si,6; Si,7; Si,8; Si,9; Si,15; Si,16; Si,17; Si,18 on the reference (i=1) and compared (i=2) 3D models respectively, are in complete agreement for one of the given positionings and are therefore considered identical and marked accordingly (for example, with a color, i.e., blue). The faces Si,1; Si,2; Si,11; Si,13 and the face S1,10; S1,14 with the face S2,10 of the 3D models are initially matched with a low confidence level, which is increased during reconciliation. These faces are finally marked as geometrically identical with different topologies (for example, using the color green) because they share the same geometric characteristics (axis and radius for cylindrical faces S1,10; S1,14 and 52,10 and normal and position for planar faces Si,1; Si,2; Si,11; Si,13) but differ on other values ​​of the descriptors that characterize their topology (perimeter for example in our descriptors).Matching and reconciling the descriptors of face Si,13 in the compared 3D models does not allow us to conclude with high confidence that it is the same modified face. At this stage, few unreconciled faces remain, and this is the best remaining option. In this case, the Type characteristic is identical and, depending on the confidence level threshold, will be declared "reconciled and different" (e.g., purple) or simply "unreconciled and new" (e.g., red). The faces are different in both their geometry and topology for all the considered repositionings. Faces S2,19 and S2,20, however, are declared "unreconciled," and therefore new (red).

[0076] In another example, the operator selects, via interface 44 ( figure 3), a part of the 3D model (which de facto becomes the reference 3D model), representing the functional faces or faces in contact with other components (interfaces), for example, faces S1,5; S1,1; S1,18, which also serve to define a positioning for the reconciliation step, as well as faces S1,6; S1,4. To facilitate the interpretation of the results, it is requested that identical faces in the found 3D models be marked in blue, that reconciled and geometrically identical faces with different topologies be marked in cyan, and finally that the other faces (not taken into account, not reconciled, new) be grayed out and transparent. With the 3D models of the Figures 7A and 7B , we obtain 52,5; S2,18 identical (therefore in blue), S2,1; 52,4; S2,6 geometrically identical (therefore in cyan) and all other faces are not considered (therefore grey, transparent).

[0077] It is understood that the operator, through an appropriate interface 44, can define extremely precise constraints such as, for example, imposing the center distance and diameters of faces Si,5 and Si,6, or the parallelism and distance between Si,11 and Si13. All these constraints can be calculated from the characteristics of the descriptors presented in this implementation.

[0078] Displaying a 3D model with labeling, or with different textures or colors for particular faces, is known in the prior art.

[0079] The differences are represented by colors, transparencies, textures, hatching, annotations, or any other graphic effect that we group under the term "marking." Similarity, meaning fewer differences according to the operator's criteria, between 3D models is represented by their position in 3D space.

[0080] It is understood that a wide range of marking methods can be used. Thus, similarity is expressed by the respective positions in 3D space of the 3D models obtained by the function that calculates the similarity index, but it can also be assessed by marking the differences between the 3D models. For example, if a hole compared to a hole in the reference 3D model is smaller in diameter but has the same axis, it will be marked in a certain way; conversely, if its diameter and axis are different, this hole is marked in another way to differentiate between the two types of differences.

[0081] In some implementations, the descriptor comparison process is influenced by the definition of features of interest or, conversely, by those that are not of interest. This definition can be specified by the operator explicitly or implicitly. At step S152 of the figure 1The 3D models are selected according to the similarity indices and criteria expressed by the operator. The results are formatted and displayed in step S160. Among other possible formats, the 3D models can be displayed in a 3D space, as illustrated in the... Figure 10 Highlighting similarities and differences through labeling, as described above, and using spatial positioning of 3D models is a new approach and represents the preferred method for communicating results. It is understood that these results can also be presented in textual form (table, list, or other format).

[0082] Figure 9A shows the simplified 3D model of the figure 5and an example of an interface for defining comparison constraints. In the case presented, the complete 3D model is used for comparison, allowing multiple repositioning to determine similarity. Constraints have been imposed, such as the distance between the axes of cylinders Si,5 and Si,6 being between 120 and 150 mm, the parallel faces Si,18 and Si,5, the radius of Si,6 being 20 mm, and finally, the distance between faces Si,11 and Si,13 being greater than 10 mm. These constraints can be determined using the S48 constraint determination interface, as shown in the... figure 3 The operator has the option to define whether the entire 3D model is taken into account in the process or only a subset of it. figure 2This provides an example of the steps involved in a comparison based on the importance of attributes, such as model faces. In this case, the operator selects the subsets of the 3D model to be considered (which are also 3D models) and, for each subset, specifies whether it should be identical or only similar in the compared 3D models. The operator also chooses whether to allow multiple repositionings or impose a comparison coordinate system, either that of the 3D models or a coordinate system constructed from constraints they impose. Finally, the operator can define constraints to which they assign greater importance, such as a center-to-center distance between 120 and 150 mm for the two cylinders Si,5 and Si,6, a radius of 20 mm for cylinder Si,6, and a distance greater than or equal to 10 mm for the faces Si,11 and Si,13.The operator therefore constructs constraints directly on the explicit characteristics of the descriptors (Radius of Si,6 = 20mm) but also on implicit ones (Distances between axes Si,5 and Si,6 or parallelism between Si,18 and Si,5). Such implicit characteristics are added to the tables containing the comparison results (additional columns) to contribute to the calculation of the similarity index.

[0083] This method of defining constraints is similar to those offered by CAD systems to constrain sketches or assemblies and is known in the prior art.

[0084] It is understood that the inventory of available properties or constraints, their selection method, their arrangement and ordering, as well as how they are weighted, can be subject to a multitude of variations without altering the underlying functionality. Rather than offering a simple binary selection (distinguishing between a "retained / important" and a "not retained / not important" selection), the interface could, for example, accept a weighting value expressed as a percentage of importance or another calibrated value. Similarly, the operator can eliminate parts of 3D models that they deem insignificant for their query, such as solid bodies smaller than a certain dimension (bounding box size) or volume, or faces whose area is less than a certain percentage of the total area of ​​the 3D model (simplification of the 3D model).It is equally possible to constrain the comparison by a maximum occupancy volume (space reservation in an assembly).

[0085] During the comparison process, constraints such as the distance between the two cylinders can act as a filter (Go / No Go) or only be used for marking, indicating whether the constraint is met or not. These constraints may also be involved in calculating the similarity index, which can then be seen as an index of compliance with the constraints.

[0086] It is understood that when the constraints are modified, the entire process can be restarted or simply the calculation of the similarity index and the marking of the already selected 3D models.

[0087] In some implementations, 3D models are ordered following a comparison process based on matching and reconciling descriptors according to the calculated similarity index. Figure 10 illustrates an example of eight 3D models of a hinge part that resemble the reference 3D model (i.e., the 3D model used for comparison for the purposes of the research presented) positioned in a 3D scene (e.g., an assembly in CAD systems) following an isometric view whose viewpoint the operator can obviously change.

[0088] The reference 3D model is positioned at the origin (the bottom right corner). The blue faces (marked with an asterisk *) are included in the difference marking after comparison. The gray faces (unmarked faces) will not be marked, whether identical or different, to facilitate interpretation of the results. Similar 3D models are positioned in 3D space using criteria associated with the three axes. The markings (colors, textures, transparencies, annotations, etc.) can represent other criteria or information. In this figure, one of the axes is used to represent a similarity index. The more a 3D model differs from the reference 3D model, the greater the distance along this axis. 3D models with similarity indices are grouped along the second axis.Thus, in the first row, directly next to the reference model, two 3D models appear: one identical and the other a mirror image (planar symmetry). The second row contains the only 3D model found that differs from the reference model but satisfies all the constraints (no yellow faces, i.e., no faces marked in the attached figure). In the following rows, 3D models appear that do not satisfy all the constraints and differ increasingly from the reference model. The first 3D model in the third row has a different groove, and its faces are marked accordingly (in yellow, i.e., marked with an asterisk * in this figure). The next model differs in its four holes and bore (all faces marked with an asterisk *).

[0089] The third axis, which is not used in this example, can be used to represent other information such as versions, origin, manufacturing states, or any other classification of 3D models that is of interest to the operator.

[0090] It is understood that the reference 3D model can be derived from an existing digitized component (part or mechanism) or from a 3D model sketched more or less roughly by the operator on the screen. In the latter case, the operator may have omitted certain details that nevertheless appear among the 3D models resulting from the search. An interface allows the operator to switch from the initial reference model to one of the 3D models appearing in the search results. When the reference 3D model is substituted, the operator can optionally maintain the constraints from the previous comparison or define new ones.

[0091] It is clear that the 3D model comparison system described above can be a powerful tool for researching, analyzing, and organizing 3D models. In many industrial settings, in particular, the use of this system and the methods described above saves time throughout the product lifecycle, especially during the design and manufacturing stages.

[0092] As illustrated in the figure 11 , the system of the figure 3 can be deployed in a distributed manner across multiple computers, on the web, or on an intranet, allowing remote access by an operator to 3D model data stores, descriptor stores, search and comparison engines, tagging tools, or other add-on modules. In the example of the figure 11The search and comparison tasks are performed by a server remote from the operator's workstation. The marking and calculation engine for the associated 3D scene accompanies the search server. In another implementation, it could also be located on the client side. The 3D scene and 3D models are sent to the operator for use in a web browser, for example. Similarly, the storage of the descriptors is, according to the illustration, hosted remotely. The generation of the descriptors can constitute a separate service for the 3D model data stores. The system of the figure 11 also shows three separate remote data stores of 3D models.

[0093] Using such a system allows an operator to browse, search, and compare 3D models from a plurality of sources from a single client workstation.

[0094] The use of such a system allows an operator to upload their existing 3D models for the purpose of generating descriptors using a remote service, whether their 3D models are for search and comparison by them alone, or for search and comparison by others as well. Table 3: Result after matching 3D models Mod.3D Kind Int N Int N-1 M1 M2 M3 Bx By Bz App Index 1 Body 133839 29137 73210 35529 411546 171 100 33 2 Body 140437 31830 62259 536376 581626 222 97 33 Hint 1.00 0.98 0.96 0.92 0.12 0.83 0.87 0.98 0.99 0.85 1.1 Plan 5128 716 22053 150403 172456 170 100 0 2.1 Plan 5728 776 19562 235031 254593 221 97 0 Hint 1.00 0.94 0. 96 0.94 0.78 0.81 0.87 0.98 1.00 0.92 1.2 Plan 2968 443 12304 20464 32769 100 70 0 2.1 Plan 5728 776 19562 235031 254593 221 97 0 Hint 1.00 0.68 0.73 0.77 0.16 0.23 0.62 0.84 1.00 0.67 1.3 plan 1266 171 427 4232 4659 65 20 0 2.3 plan 2057 253 693 18133 18826 106 20 0 Hint 1.00 0.76 0.81 0.76 0.38 0.40 0.76 1.00 0.87 0.75 1.4 plan 942 188 1178 1178 2356 40 40 0 24 plan 942 188 1178 1178 2356 40 40 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.5 Cyl_int 3770 251 10367 10367 15080 40 40 30 2.5 Cyl_int 3770 251 10367 10367 15080 40 40 30 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.6 Cyl_int 1257 126 1047 1047 1257 20 20 20 2.6 Cyl_int 1257 126 1047 1047 1257 20 20 20 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.7 Plan 600 1.00 200 450 650 30 20 0 2.7 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.8 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.16 Plan 600 1.00 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.17 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.8 Plan 600 100 200 450 650 30 20 0 2.7 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.8 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 100 1.00 1.00 1.00 1.00 1.00 1.00 2.16 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.17 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 100 1.00 1.00 1.9 Plan 300 80 25 225 250 30 10 0 2.9 Plan 300 80 25 225 250 30 10 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.15 Plan 300 80 25 225 250 30 10 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.10 Cyl_ext 1108 134 861 1197 1998 35 30 6 2.10 Cyl_ext 2248 306 3748 12757 13067 60 36 34 Hint 1.00 0.66 0.61 0.37 0.17 0.27 0.73 0.91 0.28 0.56 1.11 Plan 1671 193 824 7214 8038 78 31 0 2.11 Plan 2080 250 813 17814 18627 106 29 0 Hint 1.00 0.89 0.87 0.99 0 58 0.60 0.85 0.95 0.00 0.75 2.13 Plan 2080 250 813 17814 18627 106 29 0 Hint 1.00 0.89 0.87 0.99 0 58 0.60 0.85 0.95 0.00 0.75 1.12 Cyl_ext 2249 276 4710 5686 8898 40 41 22 2.12 Cyl_ext 2272 274 4814 5693 9009 40 41 22 Hint 1.00 0.99 1.00 0.99 1.00 0.99 1.00 1.00 1.00 1.00 1.13 Plan 1671 193 B24 7214 8038 78 31 0 2.11 Plan 2080 250 813 17814 18627 106 29 0 Hint 1.00 0.89 0.87 0.99 0 58 0.60 0.85 0.95 0.00 0.75 2.13 Plan 2080 250 813 17814 18627 106 29 0 Hint 1.00 0.89 0.87 0.99 0 58 0.60 0.85 0.95 0.00 0.75 1.14 Cyl_ext 1108 134 861 1197 1998 35 30 6 2.10 Cyl_ext 2248 306 3748 12757 13067 60 36 34 Hint 1.00 0.66 0.61 0.37 0.17 0.27 0.73 0.91 0.28 0.56 1.15 Plan 300 80 25 225 250 30 10 0 2.9 Plan 300 80 25 225 250 30 10 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.15 Plan 300 80 25 225 250 30 10 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.16 Plan 600 100 200 450 650 30 20 0 27 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.8 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.16 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.17 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.17 Plan 600 100 200 450 650 30 20 0 27 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.8 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.16 Plan 600 1.00 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.17 Plan 600 100 200 450 650 30 20 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.18 Plan 3000 260 2250 25000 27250 1.00 20 0 2.18 Plan 3000 260 2250 25000 27250 100 30 0 Hint 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.-- - - - - - - - - - 2.19 Cyl_int 628 63 157 288 288 20 10 10 Hint 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.-- - - - - - - - - - 2.20 Cone_int 91 31 6 6 11 10 10 3 Hint 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Table 4: Result after matching and reconciliation of 3D models Mod.3D Kind Type_Par Gx Gy Gz Aux A1v A1z A2x Adenay A2z A3x A3y A3z Matrix Hint 1 Body 18 0.00 25.80 13.60 0.00 1.00 0.04 -1.00 0.00 0.00 0.00 0.04 1.00 2 Body 19 2.147 6.539 13. 002 0.00 1.00 -0.04 -1.00 0.00 -0.01 -0.01 0.04 0.99 2 Hint 1 0.97 0.98 0.91 0.98 1.00 1.00 0.94 1.00 1.00 1.00 1.00 1.00 1.00 0.98 1.1 Plan 0,0;1;0 0.00 35.33 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 0.00 -1.00 2.1 Plan 0,0;1;0 2.18 19.29 0.00 -0.01 1.00 0.00 -1.00 -0.01 0.00 0.00 0.00 1.00 2 Hint 1 1.00 0.98 0.92 1.00 0.99 1.00 1.00 0.00 1. 00 1.00 1.00 1.00 0.00 0.85 1.2 Plan 0,0;1;30 0.00 -5.95 30.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2.2 Plan 0,0;1;30 -1.17 -8.75 30.00 1.00 0.03 0.00 0.03 -1 00 0.00 0.00 0.00 -1.00 1 Hint 1 1.00 0.99 0.99 1.00 1.00 0.98 1.00 0.98 0.00 1.00 1.00 1.00 0.00 0.85 1.3 plan 0;.27;.96;39.8 0.00 70.44 21.30 0.00 0.96 -0.28 -1.00 0.00 0.00 0.00 0.28 0.96 2.3 plan 0,0.164,0.986;27.948 0.00 50.15 19.97 0.00 0.99 -0.16 -1.00 0.00 0.00 0.00 0.16 0.99 2 Hint 1 0.00 1.00 0.50 0.96 1.00 0.98 0.91 1.00 1.00 1.00 1.00 0.94 0.99 0.91 1.4 plan 0,0;1;20 0.00 120.00 20.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2.4 plan 0,0;1;20 0.00 120.00 20.00 1.00 0.00 0.00 0.00 1. 00 0.00 0.00 0.00 1.00 Hint 1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2 1.00 1.5 Cyl_int 0;0;0;0,0;1;20 0.00 0.00 15.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2.5 Cyl_int 0;0;0;0;0;1;20 0.00 0.00 15.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 1 Hint 1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.6 Cyl_int 0;120;0;0;0;1;10 0.00 120.00 10.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2.6 Cyl_int 0;120;0;0;0;1;10 0.00 120.00 10.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2 Hint 1 1.00 1. 00 1.00 1.00 1.00 1.00 1.00 1.00 1. 00 1.00 1.00 1.00 1.00 1.00 1.7 Plan 1;0;0;50 50.00 -20.00 15.00 0.00 0.00 1.00 0.00 1. 00 0.00 -1.00 0.00 0.00 2.7 Plan 1,0;0;50 50. 00 -20.00 15.00 0.00 0.00 1.00 0.00 1.00 0.00 -1.00 0.00 0.00 1 Hint 1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.8 Plan 0;1;0;-10 40.00 -10.00 15.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 1.00 0.00 2.8 Plan 0;1;0;-10 40.00 -10.00 15.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 1.00 0.00 1 Hint 1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.9 Plan 1;0;0;30 30.00 -5.00 15.00 0.00 0.00 1.00 0.00 1.00 0.00 -1.00 0.00 0.00 2.9 Plan 1;0;0;30 30.00 -5.00 15.00 0.00 0.00 1.00 0.00 1.00 0.00 -1.00 0.00 0.00 1 Hint 1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.10 Cyl_ext 0;0;0;0;0;1;30 22.98 16.25 15.00 -0.58 0.82 0.00 0.00 0.00 -1.00 -0.82 -0.58 0.00 2.10 Cyl_ext 0;0;0;0;0;1;30 -3.79 17.45 15.20 1.00 -0.07 0.01 -0.07 -0.99 -0.14 0.02 0.14 -0.99 1 Hint 1 1.00 0.79 0.99 0.99 0.00 0.4.1 0.99 0.97 0.51 0.57 0.55 0.64 0.51 0.71 1.11 Plan 1;0;0;10 10.00 60.00 11.97 0.00 0.99 -0.15 0.00 -0.15 -0.99 -1.00 0.00 0.00 2.11 Plan 1;0;0;10 10.00 42.88 10.59 0.00 1.00 -0.09 0.00 -0.09 -1.00 -1.00 0.00 0.00 z Hint 1 1.00 1.00 0.92 0.95 1.00 1.00 0.95 1.00 0.97 1.00 1.00 1.00 1.00 0.98 1.12 Cyl_ext 0;120;0;0;0;1;20 0.00 122.24 10.43 1.00 0.00 0.00 0.00 -1.00 0.07 0.00 -0.07 -1.00 2.12 Cyl_ext 0;120;0;0;0;1;20 0.00 122.03 10.45 1.00 0.00 0.00 0.00 -1.00 0.07 0.00 -0.07 -1.00 2 Hint 1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.13 Plan 1;0;0;-10 -10.00 60.00 11.97 0.00 0.99 -0.15 0.00 -0.15 -0.99 -1.00 0.00 0.00 2.13 Plan 1;0;0;-10 -10.00 42.88 10.59 0.00 1.00 -0.09 0.00 -0.09 -1.00 -1.00 0.00 0.00 2 Hint 1 1.00 1.00 0.92 0.95 1.00 1.00 0.95 1.00 0.97 1.00 1.00 1.00 1.00 0.98- 1.14 Cyl_ext 0;0;0;0;0;1;30 -22.98 16.25 15.00 0.58 0.82 0.00 0.00 0.00 -1.00 -0.82 0.58 0.00 2.10 Cyl_ext 0;0;0;0;0;1;30 -3.79 17.45 15.20 1.00 -0.07 0.01 -0.07 -0.99 -0.14 0.02 0.14 -0.99 1 Hint 1 1.00 0.85 0.99 0.99 0.73 0.41 0.99 0.97 0.51 0.57 0.55 0.78 0.51 0.78 1.15 Plan 1;0;0;-30 -30.00 -5.00 15.00 0.00 0.00 1.00 0.00 1.00 0.00 -1.00 0.00 0.00 2.15 Plan 1;0;0;-30 -30.00 -5.00 15.00 0.00 0.00 1.00 0.00 1.00 0.00 -1.00 0.00 0.00 1 Hint 1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.16 Plan 0;1;0;-10 -40.00 -10.00 15.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 1.00 0.00 2.16 Plan 0;1;0;-10 -40.00 -10.00 15.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 1.00 0.00 1 Hint 1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.18 Plan 0;1;0;-30 0.00 -30.00 15.00 1.00 0.00 0.00 0.00 0.00 1.00 0.00 -1.00 0.00 2.18 Plan 0;1;0;-30 0.00 -30.00 15.00 1.00 0.00 0.00 0.00 0.00 1.00 0.00 -1.00 0.00 1 Hint 1 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Table 5: Cleaned result after matching and reconciliation of 3D models (Not final) Mod.3D Kind Type_Par Gx Gy Gz Aux Aley A1z A2x Adenay A2z A3x A3y A3z Matrix Hint 1 Body 18 0.00 25.80 13. 60 0.00 1.00 0.04 -1 00 0.00 0.00 0.00 0.04 1.00 2 Body 19 20.129 30.57 13.002 0.5 0.87 -0.04 -0.88 0.5 -0.01 0.01 0.04 0.99 1 Hint 1 0.97 0.84 0.98 0.99 0.68 0.91 0.93 0.94 0.75 1.00 0.99 1 00 1.00 0.93 2 Body 19 2.147 6.539 13.002 0.00 1.00 -0.04 -1 00 0.00 -0.01 -0.01 0.04 0.99 2 Hint 1 0.97 0.98 0.91 0.98 1.00 1.00 0.94 1 00 1.00 1.00 1 00 1.00 1.00 0.98 1.1 Plan 0;0;1;0 0.00 35.33 0.00 0.00 1.00 0.00 1 00 0.00 0.00 0.00 0.00 -1.00 2.1 Plan 0;0;1;0 26.53 4159 0.00 0.49 0.87 0.00 -0.87 0.49 0.00 0.00 0.00 1.00 1 Hint 1 1.00 0.79 0.97 1.00 0.69 0.91 100 0.06 0.75 1.00 1 00 1 00 0.00 0.80 2.1 Plan 0;0;1;0 218 19.29 0.00 -0.01 1.00 0.00 -1 00 -0.01 0.00 0.00 0.00 1.00 2 Hint 1 1.00 0.98 0.92 1.00 0.99 1.00 100 0.00 1.00 1.00 1 00 1 00 0.00 0.85 1.2 Plan 0;0;1;30 0.00 -5.95 30.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2.2 Plan 0;0;1;30 -117 -8.75 30.00 1.00 0.03 0.00 0.03 -1.00 0.00 0.00 0.00 -1.00 1 Hint 1 1.00 0.99 0.99 1.00 1.00 0.98 100 0.99 0.00 1.00 1 00 1 00 0.00 0.85 2.2 Plan 0;0;1;30 3.36 -38.16 30.00 0.85 0.53 0.00 0.53 -0.85 0.00 0.00 0.00 -1.00 2 Hint 1 1.00 0.97 0.84 1.00 0.90 0.65 100 0.74 0.07 1.00 1 00 1 00 0.00 0.80 1.3 plan 0;.27;.96;39.8 0.00 70.44 21.30 0.00 0.96 -0.28 -1 00 0.00 0.00 0.00 0.28 0.96 2.3 plan 0.08;0.14;0.98;32 88 40.08 69.42 19.97 0.49 0.85 -0.16 -0.87 0.50 0.00 0.08 0.14 0.99 1 Hint 1 0.00 0.68 0.99 0.96 0.69 0.93 0.91 0.93 0.75 1.00 0.96 0.93 0.99 0.84 2.3 plan 0;0.164;0.986;27.948 0.00 50.15 1997 0.00 0.99 -0.16 -1 00 0.00 0.00 0.00 0.16 0.99 2 Hint 1 0.00 100 0.90 0.96 1.00 0.98 0.91 1 00 1.00 1.00 100 0.94 0.99 0.91 1.4 plan 0;0;1;20 0.00 120.00 20.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2.4 plan 0;0;1;20 75.00 129.90 20.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 1 Hint 1 1.00 0.40 0.95 1.00 1.00 1.00 100 1 00 1.00 1.00 1 00 1 00 1.00 0.95 2.4 plan 0;0;1;20 0.00 120.00 20.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 Hint 1 1.00 100 100 1.00 1.00 1.00 100 1 00 1.00 1.00 1 00 1 00 1.00 2 1.00 1.5 Cyl_int 0;0;0;0;0;1;20 0.00 0.00 15.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2.5 Cyl_int 0;0;0;0;0;1;20 0.00 0.00 15.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 1 Hint 1 1.00 100 100 1.00 1.00 1.00 100 1 00 1.00 1.00 1 00 1 00 1.00 1.00 2.5 Cyl_int 0;-30;0;0;0;1;20 0.00 -30.00 15.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 1 Hint 1 0.86 100 0.85 1.00 1.00 1.00 100 1 00 1.00 1.00 1 00 1 00 1.00 0.98 1.6 Cyl_int 0;120;0;0;0;1;10 0.00 120.00 10.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2.6 cyl_int 75;129,9;0;0;0;1;10 75.00 129.90 10.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 1 Hint 1 0.57 0.40 0.95 1.00 1.00 1.00 100 1 00 1.00 1.00 1 00 1 00 1.00 0.92 2.6 Cyl_int 0;120;0;0;01;10 0.00 120.00 10.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2 Hint 1 1.00 100 100 1.00 1.00 1.00 100 1 00 1.00 1.00 100 1 00 1.00 1.00 1.7 Plan 1;0;0;50 50.00 -20.00 15.00 0.00 0.00 100 0.00 1.00 0.00 -100 0.00 0.00 2.7 Plan 1;0;0;50 50.00 -20.00 15.00 0.00 0.00 100 0.00 1.00 0.00 -100 0.00 0.00 1 Hint 1 1.00 100 100 1.00 1.00 1.00 100 1 00 1.00 1.00 100 1 00 1.00 1.00 2.8 Plan 0;1;0;10 40.00 -10.00 15. 00 0.00 0.00 100 1 00 0.00 0.00 0.00 1 00 0.00 1 Hint 1 0.50 0.92 0.95 1.00 1.00 1.00 100 0.50 0.50 1.00 0.46 0.50 1.00 0.81 2.15 Plan 0.866;0.5;0;-45 -23.48 -49.33 15.00 0.00 0.00 100 0.50 -0.87 0.00 0.87 0.50 0.00 2 Hint 1 0. 00 0.50 0.81 1.00 1.00 1.00 100 0.75 0.57 1.00 0.54 D. 75 1.00 0.78 2.16 Plan -0.5;0.866;0;-35.98 -29.64 -58.66 15.00 0.00 0.00 100 -0.87 -0.50 0.00 D.5D -0.87 0.00 2 Hint 1 0.00 0.45 0.76 1.00 1.00 1.00 100 0.07 0.75 1.00 0.73 0.07 1.00 0.70 1.9 Plan 1;0;0;30 30.00 -5.00 15.00 0.00 0.00 100 0.00 1.00 0.00 -100 0.00 0.00 2.9 Plan 1;0;0;30 30.00 -5.00 15.00 0.00 0.00 100 0.00 1.00 0.00 -100 0.00 0.00 1 Hint 1 1.00 100 100 1.00 1.00 1.00 100 100 1.00 1.00 100 100 1.00 1.00 2.15 Plan 1;0;0;-30 -30.00 -5.00 15.00 0.00 0.00 100 0.00 1.00 0.00 -100 0.00 0.00 1 Hint 1 0.75 0.52 100 1.00 1.00 1.00 100 100 1.00 1.00 100 100 1.00 0.55 2.9 Plan 0.866;0.5;0;15 28.48 -19.33 15. 00 0.00 0.00 100 0.50 -0.87 0. 00 0.87 0.50 0.00 2 Hint 1 0.00 0.99 0.93 1.00 1.00 1.00 100 0.75 0.07 1.00 0.00 0.75 1.00 0.75 2.15 Plan 0.866;0.5;0;-45 -23.48 -49.33 15.00 0.00 0.00 100 0.50 -0.87 0.00 0.87 0.50 0.00 2 Hint 1 0.00 0.58 0.75 1.00 1.00 1.00 100 0.75 0.07 1.00 0.00 0.75 1.00 0.71 1.10 Cyl_ext 0;0;0;0;0;1;30 2298 16.25 15.00 -0.58 0.82 0.00 0. 00 0.00 -1.00 -0.82 -0.58 0.00 2.10 Cyl_ext 0;0;0;0;0;1;30 -3.79 17.45 15.20 1.00 -0.07 0.01 -0.07 -0.99 -0.14 0.02 0.14 -0.99 1 Hint 1 1.00 0.79 0.99 0.99 0.00 0.41 0.99 0.97 0.51 0.57 0.55 0.64 0.51 0.71 2.10 Cyl_ext 0;-30;0;0;0;1;30 -1201 -16.78 15. 20 0.90 0.44 0.01 0.54 -0.89 -0.14 -0.05 0.13 -0.59 2 Hint 1 0.75 0.72 0.84 0.99 0.07 0.75 0.99 0.73 0.56 0.57 0.59 0.65 0.51 0.69 1.11 Plan 1;0;0;10 10.00 60.00 11 97 0.00 0.99 -0.15 0.00 -0.15 -0.99 -100 0.00 0.00 2.11 Plan 0.866;-0.5;0;10 45.10 5812 10.59 0.50 0.86 -0.09 -0.04 -0.08 -1.00 -0.87 0.50 0.00 1 Hint 1 0.25 0.72 0.99 0.95 0.68 0.92 0.95 0.58 0.96 1.00 0.93 0.75 1.00 0.86 2..13 Plan 0.866;-0.5;0;-10 27.78 68.12 10.59 0.50 0.86 -0.09 -0.04 -0.08 -1.00 -0.87 0.50 0.00 1 Hint 1 0.00 0.86 0.95 0.95 0.68 0.92 0.95 0.98 0.96 1.00 0.93 0.75 1.00 0.85 2.11 Plan 1;0;0;10 10.00 4288 10.59 0.00 1.00 -0.09 0.00 -0.08 -1.00 -100 0.00 0.00 2 Hint 1 1.00 100 0.92 0.95 1.00 1.00 0.95 1 00 0.97 1.00 100 100 1.00 0.98 2..13 Plan 1;0;0;-10 -10.00 4288 10.59 0.00 1.00 -0.09 0.20 -0.09 -1.00 -1 00 0.00 0.00 2 Hint 1 0.75 0.84 0.92 0.95 1.00 1.00 0.95 100 0.97 1.00 100 100 1.00 0.96 1.12 Cyl_ext 0;120;0;0;0;1,20 2D 0.00 122 24 10.43 1.00 0.00 0.00 0.00 -1.00 0.07 0.00 -0.07 -1.00 2.12 Cyl_ext 75;129.9;0;0;0;1;20 76.01 13166 10.45 0.97 -0.50 0.00 -0.50 -0.86 0.07 -0.03 -0.06 -1.00 1 Hint 1 0.25 0.40 0.95 1.00 0.92 0.67 100 0.75 0.93 1.00 0.98 100 1.00 0.85 2.12 Cyl_ext 0;120;0;0;01;20 0.00 12203 10.45 1.00 0.00 0.00 0.00 -1.00 0.07 0.00 -0.07 -1.00 2 Hint 1 1.00 100 100 1.00 1.00 1.00 100 100 1.00 1.00 100 100 1.00 1.00 1.13 Plan 1:0;0;-10 -10.00 60.00 11 97 0.00 0.99 -0.15 0.00 -0.15 -0.99 -100 0.00 0.00 2..11 Plan 0.866;-0.5;0;10 45.10 5812 10.59 0.50 0.86 -0.09 -0.04 -0.08 -1.00 -0.87 0.50 0.00 1 Hint 1 0.50 0.56 0.99 0.95 0.68 0.92 0.95 0.98 0.96 1.00 0.93 0.75 1.00 0.87 2.13 Plan 0.866;-0.5;0;-10 27.78 6812 10.59 0.50 0.86 -0.09 -0.04 -0.08 -1.00 -0.87 0.50 0.00 1 Hint 1 0.50 0.70 0.96 0.95 0.69 0.92 0.95 0.58 0.96 1.00 0.93 0.75 1.00 0.88 2.11 Plan 1;0;0;10 10.00 4288 10.59 0.00 1.00 -0.09 0.00 -0.09 -1.00 -100 0.00 0.00 2 Hint 1 0.75 0.84 0.92 0.95 1.00 1.00 0.95 100 0.97 1.00 100 100 1.00 0.56 2.13 Plan 1;0;0;-10 -10.00 4288 10.59 0.00 1.00 -0.09 0.00 -0.08 -1.00 -100 0.00 0.00 2 Hint 1 1.00 100 0.92 0.95 1.00 1.00 0.95 1 00 0.97 1.00 100 100 1.00 0.98 1.14 Cyl_ext 0;0;0;0;0;1;30 -22.98 16.25 15. 00 0.58 0.82 0.00 0.00 0.00 -1.00 -0.B2 0.58 0.00 2.10 Cyl_ext 0;0;0;0;0;1;30 -3.79 17.45 15. 20 1.00 -0.07 0.01 -0.07 -0.99 -0.14 0.02 0.14 -0.99 1 Hint 1 1.00 0.85 0.99 0.99 0.73 0.41 0.99 0.97 0.51 0.57 0.55 0.78 0.51 0.78 2.1D Cyl_ext 0;-30;0;0;1;30 -12.01 -16.78 15.20 0.90 0.44 0.01 0.54 -0.89 -0.14 -0.05 0.13 -0.99 2 Hint 1 0.75 0.91 0.84 0.99 0.80 0.75 0.99 0.73 0.56 0.57 0.59 0.78 0.51 0. 77 1.15 Plan 1;0;0;-30 -30.00 -5.00 15.00 0.00 0.00 1.00 0.00 1.00 0.00 -100 0.00 0.00 2.9 Plan 1;0;0;30 30.00 -5.00 15.00 0.00 0.00 1.00 0.00 1.00 0.00 -100 0.00 0.00 1 Hint 1 0.75 0.52 100 1.00 1.00 1.00 1.00 100 1.00 1.00 100 100 1.00 0.95 2.15 Plan 1;0;0;-30 -30.00 -5.00 15.00 0.00 0.00 1.00 0.00 1.00 0.00 -100 0.00 0.00 1 Hint 1 1.00 100 100 1.00 1.00 1.00 1.00 100 1.00 1.00 1.00 1.00 1.00 1.00 2.9 Plan 0.866;0.5;0;15 28.48 -19.33 15.00 0.00 0.00 1.00 0.50 -0.87 0.00 0.87 0.50 0.00 2 Hint 1 0.25 0.54 0.93 1.00 1.00 1.00 1.00 0.75 0.07 1.00 0.00 0.75 1.00 0.73 2.15 Plan 0.866;0.5;0;-45 -23.48 -49.33 15. 00 0.00 0.00 1.00 0.50 -0.87 0.00 0.87 0.50 0.00 2 Hint 1 0.25 0.95 0.78 1.00 1.00 1.00 1.00 0.75 0.07 1.00 0.00 0.75 1.00 0.75 1.16 Plan 0;1;0;-10 -40.00 -10.00 15.00 0.00 0.00 1.00 100 0.00 0.00 0.00 100 0.00 2.7 Plan 1;0;0;50 50.00 -20.00 15.00 0.00 0.00 1.00 0.00 1.00 0.00 -100 0.00 0.00 1 Hint 1 0.25 0.29 0.95 1.00 1.00 1.00 1.00 0.50 0.50 1.00 0.46 0.50 1.00 D. 75 2.8 Plan 0;1;0;-10 40.00 -10.00 15.00 0.00 0.00 1.00 100 0.00 0.00 0.00 100 0.00 1 Hint 1 1.00 0.37 100 1.00 1.00 1.00 1.00 100 1.00 1.00 100 100 1.00 0.95 2.16 Plan 0;1;0;-10 -40.00 -10.00 15.00 0.00 0.00 1.00 100 0.00 0.00 0.00 100 0.00 1 Hint 1 1. 00 100 100 1.00 1.00 1.00 1.00 100 1.00 1.00 100 100 1.00 1.00 2.17 Plan 1;0;0;-50 -50.00 -20.00 15.00 0.00 0.00 1.00 0.00 1.00 0.00 -100 0.00 0.00 1 Hint 1 0.75 0.92 0.95 1.00 1.00 1.00 1.00 0.50 0.50 1.00 0.46 0.50 1.00 0.83 2.7 Plan 1;0;0;50 50.00 -20.00 15.00 0.00 0.00 1.00 0.00 1.00 0.00 -100 0.00 0.00 2 Hint 1 0.25 0.29 0.95 1.00 1.00 1.00 1.00 0.50 0.50 1.00 0.46 0.50 1.00 0.75 2.8 Plan 0;1;0;-10 40.00 -10.00 15.00 0.00 0.00 1.00 100 0.00 0.00 0.00 100 0.00 2 Hint 1 1.00 0.27 100 1.00 1.00 1.00 1.00 100 1.00 1.00 100 100 1.00 0.95 2.16 Plan -0.5;0.866;0;-35.98 -29.64 -58.66 15.00 0.00 0.00 1.00 -0.87 -0.50 0.00 0.50 -0.87 0.00 2 Hint 1 0.25 0.92 0.76 1.00 1.00 1.00 1.00 0.07 0.75 1.00 0.73 0.07 1.00 D. 75 2.17 Plan 0.866;0.5;0;-6.5 -33.30 -72.32 15.00 0.00 0.00 1.00 0.50 -0.87 0.00 0.87 0.50 0.00 2 Hint 1 0.25 0.95 0.69 1.00 1.00 1.00 1.00 0.75 0.57 1.00 0.54 0.75 1.00 0.82 1.18 Plan 0;1;0;-30 0.00 -30.00 15.00 1.00 0.00 0.00 0.00 0.00 1.00 0.00 -100 0.00 2.18 Plan 0;1;0;-30 0.00 -30.00 15.00 1.00 0.00 0.00 0.00 0.00 1.00 0.00 -100 0.00 1 Hint 1 1.00 100 100 1.00 1.00 1.00 1.00 100 1.00 1.00 100 100 1.00 1.00 2.18 Plan -0.5;0.865;0;-55.98 15.00 -55.98 15.00 0.87 0.50 0.00 0.00 0.00 -1.00 -0.50 0.87 0.00 2 Hint 1 0.25 0.88 0.87 1.00 0.92 0.67 1.00 100 1.00 0.00 0.73 0.07 1.00 0.74

Claims

1. A comparison method, carried out by a computer system, between a reference 3D model and a target 3D model for the purpose of a comparative analysis of 3D models, the method comprising: associating each of the 3D models with at least one descriptor stored in a database, said at least one descriptor comprising associated geometric characteristics that are invariant with respect to the modelling reference frame and associated geometric characteristics that are dependent on the reference frame; for the associated geometric characteristics that are invariant with respect to the reference frame, using processing means associated with the database to match parts of the reference 3D model and parts of the target 3D model; for the associated geometric characteristics that are dependent on the reference frame, for at least the parts for which matching is successful within an acceptable error margin, using the processing means to determine groups of transformations associating parts of the reference 3D model and parts of the target 3D model; using the processing means to determine at least one group of similar transformations; and using the processing means to display to the user similarities and differences between the reference 3D model and the target 3D model that are identified, at least in part, by using said at least one group of similar transformations in order to enable the user to better compare the reference 3D model with the target 3D model.

2. The method according to claim 1, wherein the associated geometric characteristics that are invariant with respect to the modelling reference frame comprise the surface area and / or the perimeter of planar faces of the model, and the associated geometric characteristics that are dependent on the modelling reference frame comprise the centre of gravity and / or the axes of moments of inertia of the planar faces.

3. The method according to claim 1 or 2, wherein the step of determining at least one group of similar transformations comprises determining at least two groups of similar transformations in order to enable comparison of similar 3D models, wherein at least one part of the reference 3D model is in a different position relative to a corresponding part of the target 3D model.

4. The method according to claim 3, wherein the differences between the 3D models are identified, at least in part, by the at least two groups of similar transformations.

5. The method according to any one of claims 1 to 4, wherein at least one descriptor of the reference 3D model is compared with at least one descriptor of the target 3D model, and the step of using the associated geometric characteristics that are invariant with respect to the reference frame to match the parts of the reference 3D model and the parts of the target 3D model is performed for said at least one descriptor of the target 3D model in order to prioritise or to limit the step of using the associated geometric characteristics that are dependent on the reference frame according to matching of said at least one descriptor.

6. The method according to any one of claims 1 to 5, wherein matching data and groups of transformations are stored in order to facilitate more targeted comparisons or subsequent comparisons.

7. The method according to any one of claims 1 to 6, wherein at least one descriptor of the reference 3D model is compared with at least one descriptor of the target 3D model, and comparison results grouped according to matching and according to the groups of transformations are presented.

8. The method according to any one of claims 1 to 7, wherein the reference 3D model compared with the target 3D model is presented with coloured or hatched faces in order to identify similarities and differences between the 3D models.

9. The method according to claim 8, wherein at least two similarities and / or differences of geometric characteristics related to the same corresponding face between these two 3D models are presented.

10. A data processing system configured to carry out the method of comparison of 3D models for the purposes of search, classification or comparative analysis of 3D models according to any one of the preceding claims.

11. A method for manufacturing a product comprising at least one part, the method comprising: a step of selecting a target 3D model based on said similarities and said differences identified by carrying out the comparison method according to any one of claims 1 to 9; and a step of manufacturing said part from said target 3D model.