Dynamic Mesh Compression via Independent Position and Motion Field Quantization
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Solution Overview
Problem
Current mesh compression technologies struggle with efficiently compressing dynamic 3D meshes with time-varying connectivity and attribute maps, particularly in real-time applications like VR and AR, as existing standards do not effectively handle these complexities.
Innovation Solution
The proposed solution involves a method for mesh processing that includes quantizing position and motion field predictions in mesh compression, using a bitstream with specific quantization step values and entropy coding, and applying these techniques to both position and motion fields to reconstruct vertex positions and motion fields, while also addressing texture coordinate compression with fine granularity quantization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If mesh compression is applied to reduce data volume, then transmission efficiency is improved, but reconstruction quality deteriorates
Solution Approach 1:
The patent applies different quantization step values to position and motion field parameters. By changing the precision parameters differently for different data types (coarser for motion field, finer for position), the system achieves efficient compression while maintaining overall reconstruction quality. This is evident in the independent quantization process where position quantization step and motion field quantization step are separately controlled.
Solution Approach 2:
The patent treats different components of the mesh data with different compression strategies. Position data receives finer quantization while motion field data receives coarser quantization. This local differentiation of quality levels allows the system to preserve critical geometric information while compressing less critical motion information more aggressively.
2Productivity
If independent quantization of position and motion field is applied, then compression efficiency is improved, but processing complexity increases
Solution Approach 1:
The patent segments the mesh compression process into independent quantization stages for position and motion field. By dividing the compression task into separate processing streams with independent quantization parameters, the system achieves better compression efficiency while managing complexity through modular processing rather than attempting joint optimization.
3Manufacturing precision
If fine granularity quantization is applied to texture coordinates, then reconstruction accuracy is improved, but data volume increases
Solution Approach 1:
The patent applies fine granularity quantization specifically to texture coordinate data with appropriate precision parameters. By adjusting the quantization step size for texture coordinates independently from position and motion field data, the system achieves accurate texture reconstruction while controlling overall data volume through selective precision application.
Data Source
AI summary
In a method, a base mesh includes a subset of a plurality of vertices of a mesh. A position prediction of a current vertex of the base mesh is determined. A motion field prediction of the current vertex of the base mesh is determined. A position prediction residue for the position prediction of the current vertex is determined based on a first quantization step value. A motion field prediction residue for the motion field prediction of the current vertex is determined based on a second quantization step value, where the second quantization step value is dependent on the first quantization step value. A position of the current vertex of the base mesh is reconstructed based on the position prediction and the position prediction residue. A motion field of the current vertex of the base mesh is reconstructed based on the motion field prediction and the motion field prediction residue.