18 results about "Texture atlas" patented technology

The invention discloses a three-dimensional Gaussian splashing and rasterization pipeline fused view synthesis system and method. The method comprises the following steps of: 1, acquiring two-dimensional Gaussian primitives; 2, converting the two-dimensional Gaussian primitive into two triangular grids, and converting the two triangular grids into a world coordinate system; 3, baking the spherical harmonic coefficient of the color and the opacity data in a texture form; 4, enabling each Gaussian primitive to correspond to a texture block on the texture image set, enabling a triangle to cover the texture block in a UV space, and obtaining a Gaussian weight; 5, based on the obtained Gaussian weight, simulating an alpha mixing behavior in the three-dimensional Gaussian splashing pipeline by using a time-based jitter anti-aliasing method; and 6, training a symbol vector field SDF branch while training, and mutually supervising and constraining the output of the symbol vector field and the output of the two-dimensional Gaussian. The three-dimensional Gaussian splash can be rendered in a traditional rasterization pipeline of the unreal engine 4, and the fidelity equivalent to that of a special training pipeline is kept.

The invention discloses a dynamic texture atlas recombination compression method and system based on airspace sparsity, and belongs to the technical field of data compression, and the method comprises the steps: obtaining a dynamic texture sequence, carrying out the feature analysis of a texture unit in the dynamic texture sequence, and generating a multi-dimensional feature vector; constructing an initial compressibility potential model for each texture unit based on the multi-dimensional feature vectors; taking the initial compressibility potential model as input, executing an iterative collaborative decision-making process, and generating an optimized texture atlas layout and partition compression strategy mapping table; according to a partition compression strategy mapping table, performing differential compression on the texture data arranged according to the optimized texture atlas layout to generate compressed texture data; and integrating and packaging the layout information of the optimized texture atlas layout, the partition compression strategy mapping table and the compressed texture data to generate a compressed data stream. According to the method, a collaborative decision-making mechanism is adopted, airspace sparsity analysis and a differential compression strategy are combined, and efficient compression of the dynamic texture atlas can be achieved.

The application discloses a dynamic texture atlas reorganization compression method and system based on spatial sparsity, and belongs to the technical field of data compression. The method comprises the following steps: acquiring a dynamic texture sequence, performing feature analysis on texture units in the dynamic texture sequence, and generating a multi-dimensional feature vector; based on the multi-dimensional feature vector, constructing an initial compressibility potential model for each texture unit; taking the initial compressibility potential model as input, performing an iterative collaborative decision process, and generating an optimized texture atlas layout and a partition compression strategy mapping table; according to the partition compression strategy mapping table, performing differential compression on texture data arranged according to the optimized texture atlas layout, and generating compressed texture data; and integrating and packaging layout information of the optimized texture atlas layout, the partition compression strategy mapping table and the compressed texture data, and generating a compressed data stream. The application adopts a collaborative decision mechanism, combines spatial sparsity analysis and a differential compression strategy, and can realize efficient compression of a dynamic texture atlas.

A method of processing an indexed group of images, the method comprises identifying a first group of images, such as a first texture atlas, and a second group of images; identifying a first image, e.g. a first texture patch, in the first group of images 61; determining a second image, in the second group of images 62, the second image being similar to the first image; and updating an index of the second image based on an index of the first image 63. The second group of images is associated with a three-dimensional representation of a scene, wherein texture points in the three-dimensional represent groups of reference images or points within the second group. The index represents relative position and attributes of points in the image. Assessing similarities comprises determining a distance between the first image and the second image; a Euclidean distance therebetween or a sum of absolute differences (SAD). The process may be iterative and before identifying the first image, reordering of the first and second groups of images based on luminance is carried out. Indices may be arranged in a z-pattern. (Fig 14a)

The application provides an AR video fusion superimposition method and system for ultra-low delay, which comprises the following steps: obtaining a display refresh event and a picture submission timestamp, calculating a next refresh time and calculating a remaining available time interval, and generating display synchronization constraint information; dynamically screening a synchronizable superimposition information set based on a remaining budget and an estimated execution time of virtual information; analyzing anchor points to generate geometric data, aggregating resources to a texture atlas, and calculating fusion weights combined with parameters such as urgency and stability; finally, the virtual and real scene pixels are weighted and fused by using a pixel shader, and the display is submitted. Through accurate control of the time window and intelligent screening of the content, the end-to-end delay is effectively reduced, the picture lag and trailing are avoided, the priority presentation of key information is ensured, and the smoothness, real-time performance and hardware energy efficiency of AR display are significantly improved.

Disclosed herein are an immersive video encoding method and an immersive video decoding method. The immersive video encoding method includes setting basic view images among input images corresponding to multiple views, generating an atlas image using the basic view images, performing encoding on the atlas image, and generating metadata about an encoded atlas image, wherein the atlas image includes a texture atlas or a depth atlas.

The texture mapping method, device, electronic equipment and storage medium provided by the application relate to the field of scanning data processing. The method is point cloud conversion processing on a grid model of a measured object to obtain point cloud data; according to the pose of each collection image of the measured object, the mapping relationship between each collection image and the point cloud data and a global texture atlas are obtained; based on the global texture atlas, the mapping relationship between each collection image and the point cloud data is corrected to obtain the corrected mapping relationship between each collection image and the point cloud data; the global texture atlas is updated according to the corrected mapping relationship between each collection image and the point cloud data to obtain an updated global texture atlas; and based on the updated global texture atlas, the grid model is converted into a texture map, so that the color misplacement, ghosting and joint fracture problems caused by the pose error are eliminated, and the realism, continuity and engineering robustness of the texture map in the high-precision three-dimensional reconstruction scene such as intraoral scanning are improved.

The invention relates to the technical field of food detection, is used for solving the problems that existing manual grading is limited by subjective factors, evaluation standards are inconsistent, repeatability is poor and efficiency is low, and particularly discloses a beef pattern grade prediction analysis method based on texture map extraction. The method comprises the following steps: acquiring surface texture spectral information of a beef sample through hyperspectral imaging, introducing a multi-angle scanning and gray-level co-occurrence matrix construction technology, identifying the uniformity and contrast ratio of the texture of a muscle region from multiple angles, constructing texture morphological characteristics with higher discrimination, and determining the texture morphological characteristics of the beef sample. According to the method, automatic identification of pattern grades and grade interval delimitation are realized, and cross verification is completed through a standard nutritional quality index and edible quality index two-dimensional detection result. And the precision and efficiency of beef pattern grade prediction are obviously improved.

The invention provides an AR video fusion and superposition method and system oriented to ultra-low delay, and the method comprises the steps: obtaining a display refresh event and a picture submission timestamp, reckoning the next refresh time, calculating a remaining available time interval, and generating display synchronization constraint information; based on the residual budget and the predicted execution duration of the virtual information, dynamically screening an information set capable of being synchronously superposed; analyzing anchor points to generate geometric data, aggregating resources to a texture atlas, and calculating a fusion weight in combination with parameters such as urgency and stability; and finally, performing weighted fusion on the virtual and real scene pixels by using a fragment shader, and submitting and displaying the virtual and real scene pixels. According to the scheme, through time window accurate control and content intelligent screening, end-to-end delay is effectively reduced, picture jamming and smear are avoided, priority presentation of key information is ensured, and the smoothness, real-time performance and hardware energy efficiency of AR display are remarkably improved.

The invention relates to the technical field of virtual reality, in particular to an enterprise culture immersive experience method and system based on virtual reality, and the method comprises the following steps: calculating to obtain rendering batch merging potential scores for independent enterprise culture exhibit model pairs in a virtual reality scene, iteratively selecting the model pair with the highest rendering batch merging potential score, and selecting the model pair with the highest rendering batch merging potential score; and the vertexes and the index data are connected in series, a unified texture image set is generated, UV coordinates of all the vertexes are re-calculated to be matched with new positions in the texture image set, and an enterprise culture scene integration model is established. According to the method, multi-dimensional calculation is carried out on the geometrical relationship and the rendering relevance of the enterprise culture exhibit models in the virtual reality scene, so that the merging priority among the models can be more efficiently determined, redundant drawing is reduced, and the utilization rate of rendering resources is improved; geometric features of vertexes and patches are extracted in a three-dimensional space, and a culture element matching relation is combined.

The invention relates to the field of fishery detection, and discloses a fishery detection method fusing a dynamic trajectory of a fishing boat and remote sensing texture map analysis, which comprises the following steps: determining a fishery capable of fishing and a corresponding navigation trajectory by performing data processing on all trajectory data of the fishing boat, performing influence acquisition on the navigation trajectory, and determining the fishing trajectory of the fishing boat; the method is used for extracting environment fingerprints of a fishing ground. And constructing an environment fingerprint potential fishery matching model, combining the environment fingerprint of the fishing fishery to determine a potential fishery, and finally recommending the potential fishery which can be used for fishing to the fishing boat. According to the method, the depth features of the remote sensing image can be combined, the problem of finding new resource points in a data sparse region is solved through a few-sample learning technology, and a prospective view angle is provided for resource monitoring.

A method of encoding an immersive image according to the present disclosure comprises classifying a plurality of view images into a basic image and an additional image, generating a plurality of texture atlases based on the plurality of view images, generating a first depth atlas including depth information of view images included in a first texture atlas among the plurality of texture atlases, and generating a second depth atlas including depth information of view images included in remaining texture atlases other than the first texture atlas.

The invention discloses a dynamic map replacement method and device, a storage medium, equipment and a program product, and the method comprises the steps: responding to a map replacement instruction, and determining a target slot in which a map needs to be replaced and a new map path in a target object; obtaining an original map on the target slot and a regional attachment of the original map; creating a texture atlas page according to the original map and determining a texture atlas area; loading a new map in the texture image set area according to the new map path; replacing the original map with the new map, and updating the new map according to the regional attachment; and binding the updated new chartlet to the target slot, and displaying the reloaded target object in the graphical user interface. According to the dynamic map replacement method provided by the invention, the plurality of slots are formed in the skeleton, the map and the skeleton are separated, and the target object is replaced by replacing the map in the target slot.

This application provides a method, apparatus, and computing device for texture remapping of a 3D model. In this method, the topological structure of the 3D model is used to divide the model into blocks, resulting in several 3D blocks. Each 3D block is unfolded into a 2D tile through conformal mapping, and initial first texture coordinates are generated for the corresponding vertices. The initial first texture coordinates of the 2D tiles are then updated according to the topological relationship of the 3D blocks. A heuristic algorithm is used to process the 2D tiles to be arranged, completing the layout and updating the first texture coordinates to obtain second texture coordinates. Finally, the original texture atlas is remapped based on the updated second texture coordinates to generate a completely new second texture atlas. This method can streamline the empty space of the texture atlas and improve the overall utilization rate of the texture atlas.

Provided is a framework for generating photorealistic 3D talking faces conditioned only on audio input. In addition, the present disclosure provides associated methods to insert generated faces into existing videos or virtual environments. We decompose faces from video into a normalized space that decouples 3D geometry, head pose, and texture. This allows separating the prediction problem into regressions over the 3D face shape and the corresponding 2D texture atlas. To stabilize temporal dynamics, we propose an auto-regressive approach that conditions the model on its previous visual state. We also capture face illumination in our model using audio-independent 3D texture normalization.

The present application relates to the field of fishing ground detection, and discloses a fishing ground detection method combining dynamic trajectory of fishing boats with remote sensing texture atlas analysis, comprising the following steps: determining the fishing ground and the corresponding navigation track that can be fished by performing data processing on all trajectory data of the fishing boat, obtaining the influence on the navigation track for extracting the environment fingerprint of the fishing ground, constructing an environment fingerprint potential fishing ground matching model, combining the environment fingerprint of the fishing ground for determining the potential fishing ground, and finally recommending the potential fishing ground that can be fished to the fishing boat. The present application can combine the deep features of remote sensing images, solve the problem of discovering new resource points in data sparse areas through few sample learning technology, and provide a forward-looking perspective for resource monitoring.

Disclosed herein are an immersive image encoding / decoding method and apparatus, and a method for transmitting a bitstream generated by the immersive image encoding method. An immersive image encoding method according to the present disclosure may include: generating geometry atlases for an immersive image; aligning the geometry atlases to make an alignment height of the geometry atlases correspond to a height of a texture atlas; and generating a geometry bitstream by encoding the aligned geometry atlases.

An electronic device and method for texturing a three dimensional (3D) model are provided. The method includes rendering a texture atlas to obtain a first set of two dimensional (2D) images of the 3D model; rendering a ground truth texture atlas to obtain a second set of 2D images of the 3D model; comparing the first set of images with the second set of images to determine a rendering loss; applying the texture sampling properties to a convolutional neural network (CNN) to incorporate the rendering loss into a deep learning framework; and inputting a 2D texture atlas into the CNN to generate a texture of the 3D module.