Geometry-Guided Attribute Compression for 3D Meshes
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Solution Overview
Problem
The storage and transmission of large visual volumetric content, such as that captured by LIDAR systems or 3-D cameras, are costly and time-consuming due to the extensive amount of data involved, including spatial and attribute information, which poses challenges for real-time communication and processing.
Innovation Solution
A system that compresses attribute information for three-dimensional meshes using geometry-guided prediction techniques, leveraging conformal mapping and adaptive selection of prediction methods to reduce data size while preserving essential geometric correlations, allowing for efficient encoding and decoding of visual volumetric content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If visual volumetric content is captured and stored with full spatial and attribute information, then data completeness and quality are improved, but storage cost and transmission time increase significantly
Solution Approach 1:
The patent extracts only the essential geometric correlation information from the full attribute data set. By identifying and removing redundant attribute information that can be predicted from geometry, the system transmits only the necessary data while maintaining visual quality, thus reducing transmission time without significant loss of information.
Solution Approach 2:
The patent performs preliminary prediction of attribute values using geometric information before transmission. By pre-calculating which attribute values can be derived from geometry and which must be transmitted, the system optimizes the data set in advance, reducing both storage requirements and transmission time while preserving essential visual information.
2Measurement precision
If all attribute information is transmitted without compression, then data accuracy is improved, but transmission efficiency and storage efficiency deteriorate
Solution Approach 1:
The patent changes the representation parameters of attribute information by expressing attributes as functions of geometric parameters rather than independent values. This parameter transformation allows the system to maintain data accuracy for essential attributes while dramatically improving transmission efficiency through reduced data volume.
Solution Approach 2:
The patent creates a compressed representation that copies only the essential attribute information needed for accurate reconstruction. By copying and transmitting only the residual difference between predicted and actual attribute values, the system maintains data accuracy while achieving high transmission efficiency.
3Quantity of substance
If geometry-guided prediction is used to compress attribute data, then data size is reduced, but computational complexity increases
Solution Approach 1:
The patent segments the attribute compression process into distinct stages: geometric correlation analysis, prediction generation, residual calculation, and transmission. By dividing the computational task into manageable segments, the system reduces overall computational complexity while achieving significant data size reduction through targeted processing at each stage.
4Reliability
If full attribute information is stored and transmitted, then visual quality is improved, but storage cost and processing time increase
Solution Approach 1:
The patent extracts and retains only the attribute information that genuinely contributes to visual quality, removing redundant data that can be accurately predicted from geometry. This extraction process maintains visual quality by preserving essential attributes while reducing processing time through smaller data volumes.
Data Source
AI summary
A system comprises a prediction module configured to predict an attribute value, such as a texture coordinate, for a vertex of a triangle in a two-dimensional (2D) attribute representation based on known positions of vertices of a corresponding triangle in a three-dimensional (3D) geometric representation. In some embodiments, the prediction module adaptively selects a prediction technique between multiple available prediction techniques based on availability of vertices information in the 3D geometric representation and in the 2D attribute representation and further based on compression efficiency and distortion minimization. The prediction module enables compression of attribute information being signaled for volumetric visual content, such as a mesh with texture.