Bezier Volume Encoding for Low-Bitrate Point Cloud Attributes
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Solution Overview
Problem
Existing methods for compressing point clouds in computer graphics face challenges in efficiently representing and transmitting volumetric attributes, particularly in achieving a balance between geometry and attribute compression while maintaining quality and reducing bit rate.
Innovation Solution
The use of volumetric representations based on B-spline functions and wavelet transforms, such as RAHT and Bézier volumes, to encode and decode point cloud attributes, allowing for efficient compression and decompression of both geometry and attributes within a data structure that reconstructs the original attributes with high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If point cloud attributes are compressed using traditional methods (truncation, discrete transforms), then bit rate is reduced, but reconstruction accuracy deteriorates
Solution Approach 1:
The patent transitions from representing attributes on discrete points to representing them as continuous volumetric functions defined over 3D space. By using volumetric parameterizations (such as spherical harmonics, radial basis functions, or polynomial expansions), attributes are no longer confined to individual points but are distributed continuously throughout the volume, enabling smoother interpolation and higher reconstruction accuracy at lower bit rates.
Solution Approach 2:
The patent changes the fundamental parameters used to represent attributes from discrete point values to continuous volumetric function parameters. Instead of storing attribute values at each point, the system stores coefficients that define volumetric functions (e.g., spherical harmonic coefficients, RBF weights, polynomial coefficients), which can then be evaluated at any point in space to reconstruct attributes with high precision.
2Productivity
If point cloud data is compressed to reduce storage and transmission size, then transmission efficiency is improved, but data quality and fidelity are degraded
Solution Approach 1:
By moving from discrete point representation to continuous volumetric representation, the system achieves more efficient compression. The volumetric functions capture the underlying continuous nature of physical attributes (color, density, temperature) more accurately than discrete sampling, allowing for lower bit rates while maintaining or improving reconstruction quality through smooth interpolation across the volume.
Solution Approach 2:
Instead of copying and transmitting individual point attribute values, the patent creates a compressed volumetric model that can generate (synthesize) attribute values on demand. The volumetric function parameters serve as a compact representation from which infinite point attributes can be reconstructed, effectively creating a generative model rather than a static copy.
3Measurement precision
If volumetric representations are used to represent point cloud attributes, then reconstruction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent manages computational complexity by carefully selecting volumetric parameterizations that balance accuracy and cost. Different basis functions (spherical harmonics, RBFs, polynomials) offer different trade-offs between reconstruction accuracy and computational requirements. The system can adaptively choose the appropriate level of complexity based on application requirements, using fewer parameters when possible to reduce computational burden while maintaining sufficient accuracy.
Data Source
AI summary
The systems and methods discussed herein implement a volumetric approach to point cloud representation, compression, decompression, communication, or any suitable combination thereof. The volumetric approach can be used for both geometry and attribute compression and decompression, and both geometry and attributes can be represented by volumetric functions. To create a compressed representation of the geometry or attributes of a point cloud, a suitable set of volumetric functions are transformed, quantized, and entropy-coded. When decoded, the volumetric functions are sufficient to reconstruct the corresponding geometry or attributes of the point cloud.