3D Model Bitstream Encoding for Repetitive Structures
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Solution Overview
Problem
Existing 3D model compression algorithms are inefficient in handling large multi-connected 3D models with repetitive structures, as they fail to effectively extract and represent redundancy in these structures, leading to suboptimal compression and transmission efficiency.
Innovation Solution
A bitstream syntax and semantics are developed for a repetitive structure discovery based compression algorithm, offering two modes for encoding transformation data: grouped instance transformation mode for error resilience and separate instance transformation mode for smaller model size, allowing adaptive selection based on application needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static 3D model compression algorithms are used, then implementation is simple, but compression efficiency is poor for models with repetitive structures
Solution Approach 1:
The patent segments the 3D model into connected components and identifies repetitive structures among them. By dividing the model into instances and patterns, the algorithm can separately encode the pattern definition and instance transformations, achieving better compression for repetitive structures while maintaining implementation feasibility through modular processing steps.
2Reliability
If transformation data is encoded in grouped instance transformation mode, then error resilience is improved, but bitstream size increases
Solution Approach 1:
The patent provides a dynamic encoding framework that allows selection between grouped instance transformation mode and separate instance transformation mode. This enables adaptive encoding where the choice of mode can be made based on specific application requirements, allowing users to prioritize either error resilience or bitstream size depending on the use case.
3Quantity of substance
If transformation data is encoded in separate instance transformation mode, then bitstream size is reduced, but error resilience deteriorates
Solution Approach 1:
The patent provides a dynamic encoding framework that allows selection between grouped instance transformation mode and separate instance transformation mode. This enables adaptive encoding where the choice of mode can be made based on specific application requirements, allowing users to prioritize either error resilience or bitstream size depending on the use case.
4Productivity
If repetitive structure discovery is implemented, then compression efficiency is improved, but algorithm complexity increases
Solution Approach 1:
The patent segments the 3D model into connected components and identifies repetitive structures among them. By dividing the model into instances and patterns, the algorithm can separately encode the pattern definition and instance transformations, achieving better compression for repetitive structures while maintaining implementation feasibility through modular processing steps.
Solution Approach 2:
The patent uses pattern matching to identify and copy repetitive structures. Once a pattern is defined, all its instances can be represented by referencing the pattern and storing only the transformation parameters, significantly reducing the amount of data that needs to be stored and transmitted while capturing the repetitive nature of the model.
Data Source
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AI summary
Typically, 3D meshes are represented by three types of data: connectivity data, geometry data and property data. An encoded 3D mesh model can be represented, transmitted and/or stored as a bitstream. While the bitstream embeds all the transformation data, it is efficient and may address several applications, where sometimes either bitstream size or decoding efficiency or error resilience matters the most. Therefore, two mode options are disclosed for how to put the transformation data of one instance, i.e. its position, orientation and scaling factor, in the bitstream. In the first mode, the position, orientation and possible scaling factor of one instance are packed together in the bitstream. In the second mode, transformation data types, for example, the positions, orientations or possible scaling factors of all instances are packed together according to the data type in the bitstream.