Data container for describing room furnishing objects

Abstract

A data container (58) for describing room furnishing objects (4, 6, 8) in an interior space (2) is revealed, comprising - an object shape data vector (68) to describe a shape (72, 74, 76) of a room furnishing object (4, 6, 8) in the interior space (2), and - an object transformation data vector (70) to describe a translation (78), a rotation (80) and a scaling (82) of the room furnishing object (4, 6, 8) within the interior space (2), - a wall shape data vector (68) to describe a shape (72, 74, 76) of a wall (16, 20) bounding the interior space (2), and - a wall transformation data vector (70) to describe the translation (78), a rotation (80) and a scaling (82) of the wall (16, 18) bounding the interior space (2), characterized by - Georeferencing data (86) for identifying a position (88, 90) of the room furnishing object (4, 6, 8) in a geographic coordinate system.

Description

[0001] The present invention relates to a data container for describing room furnishing objects according to the preamble of claim 1 and a method for mapping a 3D model of an interior space from the data container.

[0002] US 2023 / 325908 A1 discloses the creation, processing, and presentation of data containers for describing interior furnishing objects and emphasizes the need for detailed information on the material, color, dimensions, and relative position of these objects within an interior space. These attributes are essential for creating accurate and realistic 3D models of interior furnishing objects that can be used for virtual tours, interior design simulations, and other applications.

[0003] In contrast, georeferencing data is crucial in data containers used to describe outdoor objects, as the location of outdoor objects such as trees, benches, or buildings must be known. Environmental conditions like weather, vegetation, and soil composition are important factors to consider outdoors, necessary to represent outdoor objects in their real-world context and to depict potential changes due to environmental influences. Unlike indoor environments, outdoor environments must account for seasonal changes because plants grow and change with the seasons, snow can cover buildings and landscapes, and light conditions can vary significantly.For a realistic representation and use of outdoor objects, these seasonal changes must also be taken into account in data containers for describing outdoor objects, as models that represent vegetation differently in summer and winter are significantly more accurate and meaningful.

[0004] Therefore, while data containers for describing interior furnishings require detailed material and color information to realistically represent interiors, this information offers no added value in data containers for describing outdoor objects. Conversely, US 2023 / 325908A1 also reveals no added value for information describing outdoor objects in data containers for describing interior furnishings.

[0005] The purpose of the invention is to provide data containers for describing room furnishing objects.

[0006] According to one aspect of the invention, a data container for describing interior furnishing objects comprises an object shape data vector for describing the shape of an interior furnishing object, an object transformation data vector for describing the position, rotation, and scaling of the object within the interior space, a wall shape data vector for describing the shape of a wall bounding the interior space, and a wall transformation data vector for describing the position, rotation, and scaling of this wall. According to the invention, georeferencing data is integrated into this data container, which identifies the position of the interior furnishing object in a geographic coordinate system.

[0007] Among the common 3D data formats used for object description in interior design, none utilize georeferencing data. Formats such as USD and USDZ are used for displaying complex 3D scenes, AR applications, and animations, but do not support georeferencing data. Instead, they focus on the internal structure and hierarchy of 3D scenes. Similarly, glTF, often used for transferring and displaying 3D models on the web and in AR / VR applications, focuses on the efficient transfer of geometry, materials, and animations without considering georeferencing data by default. The OBJ format is widely used for exchanging 3D geometries between different software packages, but it primarily stores the geometric data of 3D objects and does not support georeferencing data.FBX, a versatile format widely used in the gaming and film industries, also lacks standard support for georeferencing data and focuses on storing geometries, materials, animations, and scene hierarchies.

[0008] The reason these formats do not include georeferencing data lies in their application area. They are primarily designed for describing 3D geometries, materials, textures, animations, and scene hierarchies, and are intended to store and manipulate 3D models within a local coordinate system. Geographic positioning is not necessary for room furnishing and the display of 3D objects indoors or for AR applications within a room. Should georeferenced information be required, custom extensions or metadata fields could be added, but this is not standard practice and requires specific customization.

[0009] Nevertheless, georeferencing data plays an important role. First, this data is globally unique, allowing a server to assess whether the transmitted data container describes an interior space that has already been recorded. This helps avoid redundancies. Second, environmental factors, such as lighting conditions within a space, can be managed. These can be disruptive to work or detrimental to objects like images, for example, by causing fading. Georeferencing data makes it possible to better control and adjust for such environmental influences.

[0010] In a further development of the specified data container, the georeferencing data for all objects within the interior design identifies the same position in the geographic coordinate system. This means that this data only needs to be stored once in the data container, significantly reducing storage requirements by avoiding redundant data. Instead of storing separate georeferencing data for each individual object, the information is stored centrally in the data container and referenced by all objects. This not only saves storage space but also simplifies the data structure and increases data processing efficiency. Another advantage is data consistency. By storing the georeferencing data only once, it is ensured that all objects within the interior design share the same geographic position.This prevents potential errors or inconsistencies that could occur if georeferencing data had to be stored and managed separately for each object. Centralized storage also simplifies data updates, as changes to the geographic location only need to be made in one place, rather than for each individual object. Furthermore, data transmission becomes more efficient. Less data needs to be sent when transferring the data container across networks, conserving bandwidth and increasing transmission speed. This is particularly beneficial for applications requiring fast data transfer, such as real-time augmented reality applications or virtual tours. Finally, centralized storage of georeferencing data enables better integration and compatibility with other systems and databases.Systems that process geographic information can easily extract and use the georeferencing data once stored, improving interoperability between different applications and platforms.

[0011] Georeferencing data refers to information that defines the geographic position of an interior space within a specific coordinate system. This data can take various forms, depending on the specific application and available technologies. Examples of georeferencing data include a postal address, preferably including the apartment number; the precise location within a cell tower; coordinates from a geographic information system (GIS); or data from a global navigation satellite system (GNSS).

[0012] In particular, the use of georeferencing data from GNSS signals offers several advantages. GNSS data provides high accuracy and reliability in determining geographic position because it is based on signals from satellites that ensure global coverage. This data is generally more accurate than other forms of georeferencing, such as cell tower positioning, which is limited by the density and distribution of cell towers. Another advantage of using GNSS data is its real-time availability. GNSS receivers can continuously provide up-to-date position data. Furthermore, GNSS data is interoperable and can be used in various applications and systems, facilitating integration and data exchange.GNSS data is also independent of local infrastructure, such as cell towers or Wi-Fi networks, which ensures its availability in remote or poorly covered areas. This independence makes GNSS data particularly valuable for applications describing interior furnishings, because the invention is thus applicable to interiors that are difficult or impossible to describe using alternative georeferencing data in remote areas.

[0013] In a further development of the specified data container, GNSS refers to the Global Positioning System (GPS). GPS is the most widely used and well-known global navigation satellite system and therefore provides reliable and accurate positioning data worldwide. Modern GPS receivers can determine positions with an accuracy of a few meters under favorable conditions. This is particularly advantageous for indoor spaces in rural areas, where other georeferencing methods are very inaccurate, but GPS tracking, due to the absence of distortions caused by shadowing and similar factors, leads to very precise results.

[0014] In another iteration of the specified data container, the georeferencing data is encrypted. This transforms the original positional data into an undecipherable string. This protects the data from unauthorized access and manipulation. Even if an attacker intercepts the encrypted data, they cannot easily reconstruct the original georeferencing data.

[0015] Using hash functions for encryption offers additional advantages regarding data integrity. Hash functions generate unique output values ​​for each input, so any change to the original georeferencing data would result in a completely different hash output. This makes it possible to verify data integrity and ensure that the data has not been tampered with during transmission or storage. Another advantage is the efficiency of hash functions. Compared to other encryption methods, hash functions are generally faster and require less computing power. This is particularly beneficial in resource-constrained environments such as mobile devices or IoT applications, where computing power and energy consumption need to be optimized. Finally, encrypted georeferencing data also offers advantages in terms of data processing and analysis.Hash encryption allows georeferencing data to be securely stored and processed in large databases without revealing the underlying sensitive information. This enables the development of secure and privacy-friendly analyses and applications based on aggregated or anonymized location data.

[0016] In another training course, the specified data container is created in the JavaScript Object Notation format called JSON. JSON is a lightweight data format that is easy to read and write, simplifying application development and maintenance. Because JSON is text-based, it can be easily read and understood by humans, simplifying debugging and data analysis. Furthermore, JSON is natively supported in almost all modern programming languages, enabling seamless integration into existing systems and applications. Another advantage of JSON is its flexibility and extensibility. Data structures can be easily adapted and extended without affecting the existing data structure. This is particularly useful in rapidly evolving projects where requirements change frequently.The JSON format allows for the addition of extra fields or changes to the structure without causing compatibility issues. Data transfer efficiency is also a significant advantage. JSON files are generally more compact than, for example, XML files, resulting in faster transfer speeds and lower bandwidth usage. This is particularly important in applications that need to transfer large amounts of data over resource-limited networks, such as mobile networks, especially in rural areas.

[0017] According to a further aspect of the invention, a method for mapping a 3D model of an interior space from one of the specified data containers comprises the steps of creating the 3D model based on the object shape data vector, the object transformation data vector, the wall shape data vector and the wall transformation data vector, determining a field information based on the georeferencing data, and mapping the 3D model linked with the field information.

[0018] Outdoor information refers to data that describes the context and surroundings of an interior space. This information typically includes geographic coordinates, environmental conditions such as weather and vegetation, and seasonal changes. It may also include topographic details, proximity to other buildings or natural landscape features, and other relevant environmental factors.

[0019] Linking outdoor information with the 3D interior model allows for a more realistic and contextualized representation of the interior. This means the interior is not viewed in isolation, but rather depicted within its actual geographical and environmental context. This realistic representation is particularly important for a compelling property presentation, where external factors also influence the prospective buyer's or renter's decision. This realistic representation includes, for example, environmental influences on the interior, such as humidity. This is not only useful for the buyer or renter decision but also for planning any necessary modernization and / or maintenance work.

[0020] In a further development of the aforementioned method, the outdoor data includes the wall's exposure to the sun. A key advantage is the ability to precisely simulate the effects of sunlight in the interior. By knowing how the sun strikes the walls of a room at different times of day and year, the natural lighting within the interior can be accurately modeled. This is particularly valuable for architects and interior designers who, with knowledge of the lighting conditions, must consider environmental effects such as seasonal temperature fluctuations when furnishing an interior. This allows the selection of building materials to be optimized to capture as much solar heat as possible in winter and to avoid excessive heat in summer. This can lead to a significant reduction in heating and cooling costs and thus also reduce a building's ecological footprint.In this way, the indoor climate and living comfort can also be improved, because by taking sun exposure into account, a uniform and pleasant temperature can be maintained in the interior.

[0021] In a preferred refinement of the described method, the wall shape data vector further describes the shape of a window in the wall enclosing the interior space. This allows for precise simulation of solar exposure. Knowing the exact shape and position of windows makes it possible to accurately model the paths of natural light entering the room through these windows. This helps to avoid potential damage from solar radiation. Direct sunlight can cause interior materials to fade or become damaged. By simulating solar exposure, taking window shape into account, sensitive materials or valuable works of art can be positioned to protect them from direct sunlight.Furthermore, measures such as the use of UV-protective films or sunshades can be specifically planned and implemented to protect the interior from harmful UV radiation. Another advantage lies in the improved use of augmented and virtual reality (AR and VR). Precise knowledge of the window shape contributes to increased realism and greater immersion. Users can realistically experience the lighting conditions and atmosphere of a room at different times of day. This level of detail is particularly beneficial for virtual property tours or architectural visualizations, as it allows for a more accurate representation of lighting effects and the associated mood of a room.The realistic simulation of the lighting conditions entering the room through the windows increases the credibility and appeal of the virtual environment and helps users to get a better idea of ​​the actual conditions on site.

[0022] According to another aspect of the invention, a control device is provided to carry out one of the specified methods.

[0023] In a further development of the specified device, the device comprises a memory and a processor. The specified method is stored in the memory in the form of a computer program, and the processor is provided for executing the method when the computer program is loaded from memory into the processor.

[0024] According to another aspect of the invention, a computer program comprises program code means to perform all steps of the specified method when the computer program is executed on an electronic device or one of the specified devices.

[0025] According to another aspect of the invention, a computer program product includes program code that is stored on a computer-readable data carrier and which, when executed on a data processing device, performs the specified method.

[0026] The properties, features, and advantages of this invention described above, as well as the manner in which they are achieved, will become clearer in connection with the following description of the exemplary embodiments, which are explained in more detail in conjunction with the drawings. The drawings show: Fig. 1 a schematic representation of an interior space with furnishing objects, Fig. 2 a structural representation of photogrammetry software, Fig. 3. A structural representation of a data container to describe the interior of the Fig. 1, and Fig. 4. A system for mapping a 3D model of the interior from the data container of the Fig. 3.

[0027] In the figures, identical technical elements are labelled with the same reference symbols and described only once. The figures are purely schematic and, above all, do not represent the actual geometric relationships.

[0028] It will be on Fig. Reference is made to Figure 1, which shows a schematic representation of an interior space 2 with furnishings in the form of a desk 4, an office chair 6, and a wall picture 8. For the sake of clarity, the interior space 2 is to be considered as being spanned by a longitudinal direction 10, a transverse direction 12 perpendicular to the width direction 10, and a vertical direction 14 perpendicular to the longitudinal direction 10 and perpendicular to the transverse direction 12.

[0029] Interior space 2 is bounded by walls, of which in Fig. Figure 1 shows only a rear wall 16, viewed lengthwise, with a window 18 and a side wall 20 running perpendicular to it. A light source 22, in the form of the sun, is indicated by the light source 22 shining through the window 18. However, other natural light sources, such as the moon, or artificial light sources, such as a street lamp, could also be considered as the light source 22. This will be discussed in more detail later. On the desk 4, work objects in the form of a book 24 and a computer 26 are shown for illustrative purposes; these will be explained in more detail later in connection with the light source 22.

[0030] Viewed in the longitudinal direction 10, at the beginning of the interior space 2, a person 28 stands against the side wall 20 with a video recording device 30, which could be, for example, a video camera or a smartphone. The person 28 occupies a Fig. 2 referenced video 32 of the interior 2, by rotating around its own axis aligned in the height direction 14. This video 32 is then converted into a 3D model 36 using photogrammetry software 34, which is shown below by means of Fig. 2 will be explained in more detail, which shows the essential components of the photogrammetry software 34 in a structural view.

[0031] First, a feature detection module receives 38 frames, or 2D images, extracted from the video 32. These frames serve as the basis for identifying distinctive points. The output of the feature detection module 34 consists of the detected distinctive points 40 in the frames of the video 32. Typical distinctive points can be corners, edges, or other visually prominent features. The detected distinctive points 40 are then passed to a feature matching module 42, which uses an algorithm to calculate mapping information 44 from the distinctive points 40 between the different frames of the video 32. This mapping information 14 describes the mapping of the distinctive points 40 from the feature matching module 42. This mapping information 14 enables the determination of a spatial relationship between the frames of the video 32.The feature-matching module 42 is followed by an image alignment and registration module 46, which uses the mapping information 44 to determine spatially aligned and registered images 48. These registered images 48 are able to provide consistent information about the positions of the landmark points 40. Subsequently, a triangulation and 3D reconstruction module 50 receives the spatially aligned and registered images 48 as well as camera parameters 52 and determines at least one 3D mesh model 54 from them. This mesh model describes the 3D positions of the landmark points 40 and reconstructs the three-dimensional structure of the spatial objects shown in the video 32 in the form of a point cloud. Finally, a texturing and rendering module 56 receives the 3D mesh model 54, the frames of the video 32, and the mapping information 44 and uses this information to generate the textured 3D model 36.The texturing is therefore based on the frames of video 32 and is carried out on the basis of the.

[0032] Assignment information 44, to create a realistic appearance of the three-dimensional structure of the room furnishing objects shown in video 32 in the form of the desk 4, the office chair 6 and the wall picture 8.

[0033] The in Fig. The structure shown in Figure 2 represents the basic process in photogrammetry, in which information from the frames of video 32 is converted into a complete 3D model 36 of the room furnishing objects and is basically known, for example, from WO 2016 / 005 412 A1.

[0034] The 3D model 36 is then stored in a data container 58 and accessed via a Fig. The network 60 indicated in the diagram is transmitted to a server, which then makes the 3D model 36 available for further use. Before discussing this retrieval in more detail, we will first examine the following: Fig. 3 the structure of the data container 58 which stores the 3D model 36 will be explained in more detail.

[0035] The data container 58 is described below in JSON format as an example and without limitation. It is a hierarchical, tree-like data structure consisting of various elements. The data structure begins with a root 64, which forms the basis for the 3D model 36. Analogous to a file system or an LDAP system, the root 64 is connected to nodes via branches. These nodes can either be the root themselves, have further branches, or carry a variable value. In this way, the data container 58 describes an array, where nodes with subbranches describe subarrays.

[0036] In data container 58, each detected room furnishing object 4, 6, 8, 16, 20 now has its own node 66 created, whereby in the Fig. For the sake of clarity, a separate node 66 for the office chair 6 has been omitted. Each node 66 contains the 3D mesh model 54 of its associated room furnishing object 4, 6, 8, 16, 20 in the form of a shape data vector 68 and a transformation data vector 70.

[0037] The shape data vectors 68 refer to the geometric description of the room furnishing objects 4, 6, 8, 16, 20 in the 3D model 36. They contain all the information that represents the structure and shape of the respective room furnishing object 4, 6, 8, 16, 20, such as the coordinates of the vertices 72 (vertex data), faces 74 (polygon data), and edges 76 of the 3D mesh model 54. The shape data vectors 68 are used in the data container 58 to store and represent the specific 3D model 36 of the respective room furnishing object 4, 6, 8, 16, 20. A shape data vector 68 is therefore understood to be a data set that describes and stores the geometric structure and shape of a 3D object, for example, in the form of vertices 72, edges 74, and faces 76.

[0038] In contrast, the transformation data vectors 70 contain the information necessary to describe the position, orientation, and scale of the respective room furnishing objects 4, 6, 8, 16, 20 within the interior space 2. This information includes, for example, translations 78 (displacements), rotations 80 (rotations), and scalings 82. The transformation data vectors 70 are therefore used to correctly position the room furnishing objects 4, 6, 8, 16, 20 within the interior space 2. A transformation data vector 70 is thus defined as a data set that describes and stores the position, orientation, and scale of a 3D object in space by including, for example, translations 78, rotations 80, and scalings 82.

[0039] In addition to these vectors 68, 70, each object can be described with further subnodes 84, which include, for example, further vectors to describe material properties or parameters to describe physical properties.

[0040] The material properties could include texture information, colors, reflection and transparency properties that influence the appearance of the respective room furnishing object 4, 6, 8, 16, 20 in the 3D model 36. These vectors can include information such as the type of surface (e.g., glossy or matte), the color (through RGB values ​​or other color schemes), and the texture (e.g., wood, metal).

[0041] The physical properties could include parameters such as density, elasticity, thermal conductivity, or acoustic behavior, which simulate the behavior of the respective room furnishing object 4, 6, 8, 16, 20 under various conditions. These parameters are particularly important for applications in simulation and analysis, where the physical behavior of the room furnishing objects 4, 6, 8, 16, 20 must be taken into account.

[0042] In the present version, the data container 58 additionally contains a node 66 in which georeferencing data 86 for the interior space are stored. While it would be possible in principle to assign this georeferencing data 86 separately to each of the individual room furnishing objects 4, 6, 8, 16, 20, this is suboptimal from the perspective of the necessary storage requirements and the transfer of the data container 58 to the server 62, especially if the bandwidth of the network 60 is limited.

[0043] The georeferencing data 86 can be stored in the data container 58 as a subarray or as a variable.

[0044] This is application-dependent. For georeferencing data 86 in the form of postal addresses, an array with several subnodes is suitable, each storing the respective address lines. In the present implementation, the georeferencing data 86 is stored as longitude 88 and latitude 90, as obtained from GPS localization. This type of georeferencing data 86 is independent of international conventions and can, for example, be encrypted using a standardized hash value.

[0045] An example of using georeferencing data 86 as a variable would be if the hash value were stored directly for georeferencing data 86.

[0046] Finally, we will use the following examples: Fig. 4 a system 90 is explained that can be used to map the 3D model 36 of the interior space 2 with the room furnishing objects 4, 6, 8, 16, 20 from the data container 58.

[0047] System 90 includes a data interface 92 for receiving the data container 58, which forwards the data container 58 to a corresponding evaluation device 94 for the reconstruction of the 3D model 36 of the interior 2. The evaluation device 94 forwards the reconstructed 3D model 36 of the interior 2 to a suitably designed display device 96, such as a screen, for presentation.

[0048] The evaluation device 94 then forwards the data container with the georeferencing data 86 to a facility 98, which is configured to extract additional information for the 3D model 36 from the georeferencing data 86. The facility 98 sends the georeferencing data 86 to another server 100, which returns field information, in this case in the form of sun position data 102, to the georeferencing data 86. The facility 98 calculates additional 3D data from this sun position data 102, which is displayed on the display device 96 as shadows 104 cast by the individual room furnishing objects 4, 6, 8, 16, and 20.

[0049] The shadows 104 provide the viewer of the 3D model 36 on the display device 96 with valuable information about environmental influences from the outdoor area, such as solar radiation from the sun 22. For example, the viewer can estimate how the work at the desk 4 will likely be if it is exposed to sunlight as shown in the diagram. Fig. The location shown is as follows: While good lighting conditions prevail for reading the book (24), working on the computer (26) would likely be very difficult because the computer screen (26) reflects the sun (22) very strongly, making it impossible to see anything. The mural (8), on the other hand, hangs in an area protected from the sun and should not be damaged by sunlight at this location. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] US 2023 / 325 908 A1 [0002, 0004] WO 2016 / 005 412 A1

[0033]

Claims

[1] Data container (58) for describing room furnishing objects (4, 6, 8) in an interior space (2), comprising - an object shape data vector (68) to describe a shape (72, 74, 76) of a room furnishing object (4, 6, 8) in the interior space (2), and - an object transformation data vector (70) to describe a translation (78), a rotation (80) and a scaling (82) of the room furnishing object (4, 6, 8) within the interior space (2), - a wall shape data vector (68) to describe a shape (72, 74, 76) of a wall (16, 20) bounding the interior space (2), and - a wall transformation data vector (70) to describe the translation (78), a rotation (80) and a scaling (82) of the wall (16, 18) bounding the interior space (2), characterized by - Georeferencing data (86) for identifying a position (88, 90) of the room furnishing object (4, 6, 8) in a geographic coordinate system. [2] Data container (58) according to claim 1, wherein the georeferencing data (86) for all room furnishing objects (4, 6, 8) of the interior space (2) indicate the same position (88, 90) in the geographic coordinate system. [3] Data container (58) according to claim 1 or 2, wherein the georeferencing data (86) are from the signals of a global navigation satellite system called GNSS. [4] Data container (58) according to claim 3, wherein the GNSS is a Global Positioning System called GPS. [5] Data container (58) according to one of the preceding claims, wherein the georeferencing data (86) are encrypted, preferably with a hash function. [6] Data container (58) according to one of the preceding claims, created in the JSON JavaScript Object Notation. [7] Method for mapping a 3D model (36) of an interior space (2) from a data container (58) according to one of the preceding claims, comprising: - Creating the 3D model (36) based on the object shape data vector (68), the object transformation data vector (70), the wall shape data vector (68) and the wall transformation data vector (70), - Determining field information (102) based on the georeferencing data (86), and - Mapping the 3D model (36) linked with the field information (102). [8] Method according to claim 7, wherein the field information (102) comprises an exposure of the wall (16, 20) to the sun (22). [9] Method according to claim 7 or 8, wherein the wall shape data vector (68) further describes a shape of a window (18) in the wall (16, 20) enclosing the interior space (2). [10] Device (90) configured to carry out a method according to any one of claims 7 to 9.

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