78 results about "Geometric reconstruction" patented technology

The invention provides a three-dimensional building fine geometric reconstruction method integrating airborne and vehicle-mounted three-dimensional laser point clouds and streetscape images. The method comprises the following steps: (1) a quick modeling method based on airborne laser data; (2) combining the semantic segmentation framework of the vehicle-mounted point cloud and the image; and (3) amodel automatic enhancement algorithm fusing multi-source data. According to the method, airborne laser point cloud, vehicle-mounted laser point cloud and streetscape images are taken as research objects, model reconstruction, model enhancement and updating are taken as targets, joint processing of point cloud and image data of different platforms is realized, and the fusion potential of variousdata is fully mined. The final research result will perfect the fusion and fine modeling framework of vehicle-mounted-airborne data, promote the development of the point cloud data semantic segmentation technology, and serve the emerging application fields of unmanned driving and the like.

Systems, computer program products, and techniques for reconstructing objects depicted in digital image data within a three-dimensional space are disclosed, according to various exemplary embodiments. The inventive concepts uniquely utilize internal features to accomplish reconstruction, thereby avoiding reliance on reconstructing objects based on information derived from location of edges. The inventive concepts thus provide an improvement over conventional object reconstruction since objects may be reconstructed even when edges are obscured or not depicted in the digital image data. In one aspect, a computer-implemented method of reconstructing an object depicted in a digital image includes: detecting a plurality of identifying features of the object, wherein the plurality of identifying features are located internally with respect to the object; and reconstructing the digital image of the object within a three dimensional coordinate space based at least in part on some or all of the plurality of identifying features.

Systems, computer program products, and techniques for detecting objects depicted in digital image data are disclosed, according to various exemplary embodiments. The inventive concepts uniquely utilize internal features to accomplish object detection, thereby avoiding reliance on detecting object edges and/or transitions between the object and other portions of the digital image data, e.g. background textures or other objects. The inventive concepts thus provide an improvement over conventional object detection since objects may be detected even when edges are obscured or not depicted in the digital image data. In one aspect, a computer-implemented method of detecting an object depicted in a digital image includes: detecting a plurality of identifying features of the object, wherein the plurality of identifying features are located internally with respect to the object; and projecting a location of one or more edges of the object based at least in part on the plurality of identifying features.

The invention provides a method for accurately reconstructing a microcosmic finite element grid model of a dissimilar material on the basis of CT (computed tomography) images. According to the method, sequence CT images are acquired through industrial CT, and micro-structural information in the CT images is mapped onto the reconstructed finite element grid model on basis of digitization and threshold segmentation, so that any detailed structural information in the dissimilar material can be represented in the reconstructed model. The method improves the reconstruction accuracy by means of contrast-limited adaptive histogram equalization, median filtering and pixel interpolation, and improves the reconstruction efficiency through image cut and pixel combination. With the method, rectangular (two-dimensional) and cuboid (three-dimensional) unit grid models with higher finite element analysis accuracy are directly reconstructed, error accumulation during reconstruction, grid partition and other links of the existing geometric reconstruction method is avoided, and reconstruction accuracy and efficiency are improved. The method can be widely applied to fields such as performance prediction and optimization design of dissimilar materials.

In various embodiments, methods, systems, and computer program products for processing digital images captured by a mobile device are disclosed. Myriad features enable and/or facilitate processing of such digital images using a mobile device that would otherwise be technically impossible or impractical, and furthermore address unique challenges presented by images captured using a camera rather than a traditional flat-bed scanner, paper-feed scanner, or multifunction peripheral. Notably, the presently disclosed systems and techniques enable three-dimensional reconstruction of objects depicted in image captured using a camera of a mobile device. The reconstruction corrects or compensates for perspective distortion caused by camera-based capture.

The embodiment of the invention provides a method for reconstructing a three-dimensional model of high dynamic range texture, comprising the following steps: adopting shooting devices arranged in a grouped encircling array mode to shoot multi-viewpoint and multiple exposed images and/or videos; restoring response curves of the shooting devices; performing exposure alignment on the images shot at different exposure time so as to restore the three-dimensional model based on the response curves of the devices; and restoring high dynamic texture information by calculating color information of each vertex of a grid. The embodiment of the invention also discloses a device for reconstructing the three-dimensional model of high dynamic range texture, which is composed of an acquisition module, a response curve restoring module, an exposure alignment module and a reconstructing module. The method and the device for reconstructing the three-dimensional model of high dynamic range texture can realize multi-viewpoint stereo reconstruction for the three-dimensional model of high dynamic range under the premise of not lowering geometric reconstruction precision of the model without additionally updating the hardware equipment.

The invention discloses a method for pre-estimating an equivalent thermal conductivity of an unidirectional fiber toughening composite material based on gap defect identification, wherein the method mainly comprises the following steps: processing a material micro electron microscope image by using a picture identification technology, and identifying material internal structure characteristics and gap defects; according to a fiber volume fraction ratio stability criterion, applying a geometric reconstruction technology, establishing a representative element geometric topology model, and simultaneously determining the gap defects; through a method of adding a thermal contact resistance at a representative element internal interface, introducing influence of the gap defects; and simulating to obtain the equivalent thermal conductivity by using an finite element method. According to the method, the representative element considering the gap defects is established based on image identification of an actual micro structure, and calculation of the equivalent thermal conductivity is carried out, an influence rule of gap defect position, number, thickness and other characteristic parameters on the equivalent thermal conductivity is analyzed, and a technical reference is provided for high precision pre-estimation of the thermal conductivity of same-type composite materials.

The invention belongs to the field of composite plastic forming, and particularly provides a virtual test method of the plastic forming property of a particle reinforced aluminum matrix composite. The method includes the steps that (1) geometric reconstruction is conducted on a microstructure of the particle reinforced aluminum matrix composite; (2) meshes of a geometric model, established in the step (1), of the microstructure are divided through an object-oriented finite element technology, and the model is restricted; (3) in finite element software, material attributes are given to the meshes in the step (2), and a matrix damage model, a particle breakage damage model and an interface debond damage model are coupled; (4) the plastic forming property test experimental process is simulated in finite element simulation software, and then plastic forming property indexes of the composite are acquired. According to the method, the plastic forming property of the particle reinforced aluminum matrix composite can be accurately and virtually predicted, experimental test cost of the plastic forming property of the composite can be lowered, operation is easy to conduct, and convenience is brought for technical staff to use.

The invention provides a reconstruction algorithm for a finite element discretized geometric model. The reconstruction algorithm comprises the following steps: firstly, inputting a finite element discrete grid to determine a grid topological relation, and adding a steady-state thermal field value to a grid node by solving a Laplace partial differential equation; secondly, extracting surface gridsand boundaries of a model, and setting a reasonable threshold value according to a geometric discrete boundary vector value to determine an isothermal surface; performing overlapping removal, extracting an isotherm and a domain boundary, setting an objective function to perform boundary intersection operation, judging the parity of sub-domains according to an intersection result, and setting an objective function for different sub-domains to perform reasonable quadrilateral sub-domain division; and finally, carrying out discrete boundary sorting and boundary fairing on the sub-domains, obtaining control points in the sub-domains through linear interpolation to realize B-spline parameterization, and verifying the accuracy of reconstructed geometry by utilizing isogeometric analysis. Exampleresults show that the algorithm provided by the invention well solves the geometric reconstruction problem of the finite element model.

The invention discloses a BIM (Building Information Modeling) technology-based scene construction method, which relates to the technical field of garden landscape model manufacturing, and comprises the following steps of obtaining a group of site photos at multiple angles, importing the photos into Agisoft Metaphapp Pro software for processing, extracting image information by the software, and converting the image information into point cloud; editing and arranging the generated point cloud data, classifying the point information and geometrically reconstructing the point information, and storing the point cloud data in a file format of. LAS; generating a DSM/DTM model according to projection calculation; correcting the texture color of the DSM/DTM model; exporting the model into a. Obj format; importing the model in the. Obj format into 3D software for processing. According to the method for exporting the model in the SU format from the 3D software, the two kinds of software, namely the Agisoft Metaphapp Pro and the 3D software, are reasonably applied, organic combination of the two kinds of software is achieved, the advantages are complementary, and then manufacturing of a realistic site is achieved.

The embodiment of the invention provides a three-dimensional human body reconstruction method and device, equipment and a storage medium. The method comprises the steps: carrying out the geometric reconstruction of a human body based on a single human body image of a target human body, and obtaining a three-dimensional grid model of the target human body; based on the single human body image, performing local geometric reconstruction on a local part of the target human body to obtain a three-dimensional grid model of the local part; fusing the three-dimensional grid model of the local part with the three-dimensional grid model of the target human body to obtain an initial three-dimensional model; and performing human body texture reconstruction according to the initial three-dimensional model and the single human body image to obtain a three-dimensional human body model of the target human body. According to the embodiment of the invention, the local part in the three-dimensional grid model of the target human body is clearer and more accurate, and the reconstruction effect of the local part is improved.

The invention discloses a geometric shadow map method for triangle reconstruction, and the method comprises the steps: (1) drawing a scene in a light source space, and generating and storing a geometric shadow map; (2) carrying out the compression of the obtained geometric shadow map based on a GPU, and obtaining a compressed geometric shadow map; (3) drawing a scene in a vision field, packaging pixels, carrying out triangle consistency check, and using a stored triangle to achieve the geometric reconstruction of the depth value of a pixel shading object; (4) comparing a pixel depth value in the light source space with the depth value of the shading object, obtaining a shadow calculation result, and completing the reconstruction of the geometric shadow map. Aiming at problems of deformation and depth deviation in a shadow map method, the method employs discrete scene raw triangular patches to represent and record the information of the shading object, and the invention provides the geometric shadow map method. While generating a high-quality shadow, the method has certain advantages over other algorithms in geometric data storage and expenditure and performance, and can meet the demands of real-time application better.

The invention provides a multi-view stereoscopic vision body surface positioning tracking method. The multi-view stereoscopic vision body surface positioning tracking method comprises the following steps that S1, calibrating cameras in each group of vision assemblies; S2, performing simulation geometric reconstruction on the CT data: during reconstruction, retaining point cloud data and target point information of a body surface in the CT data, and simulating a spatial position of the CT point cloud data in a left camera coordinate system; S3, collecting body surface point cloud data through visual components on the two sides of the treatment bed; S4, projecting grating stripes to the surface of the human body by each group of structured light projectors, and modulating the phases of the grating stripes; Carrying out phase splitting by a computer; Obtaining three-dimensional information of the body surface; S5, fusing the point cloud data collected on the two sides of the body surface;And S6, performing three-dimensional accurate registration on the collected and fused body surface point cloud data and CT reconstructed body surface point cloud data. The multi-view stereoscopic vision body surface positioning tracking method has no radiation damage to human bodies and is low in equipment cost.

The invention provides a real-time large-scene three-dimensional semantic modeling method. The method comprises the following steps of S1, constructing a three-dimensional geometric model through an RGB image and a depth image obtained by scanning a scene through a sensor; s2, inputting the three-dimensional geometric model into a three-dimensional convolutional neural network to complete semanticsegmentation; s3, integrating semantic tags output by the three-dimensional convolutional neural network into the three-dimensional geometric model to complete semantic modeling; wherein the step ofconstructing the three-dimensional geometric model and the step of semantic segmentation are combined in a multi-thread mode and are carried out at the same time. Joint real-time three-dimensional geometric reconstruction and semantic reconstruction are realized; by adopting the sparse convolutional neural network and accelerating the calculation of the convolutional network, the performance of real-time operation can be achieved; according to the method, two-dimensional convolution in UNet is replaced by three-dimensional convolution, and meanwhile, the depth of the UNet network is increased,so that the capacity of the convolutional network is larger, and the semantic segmentation accuracy is improved.