Point Cloud Geometry Compression Using Hyperprior Occupancy Prediction
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Solution Overview
Problem
Existing point cloud compression techniques are inefficient in handling large-scale point clouds, especially for consumer devices with limited computational power, and lack effective methods for lossy coding while maintaining reconstruction accuracy.
Innovation Solution
A learning-based point cloud compression framework that utilizes a bottom-up strategy with neural networks to estimate voxel occupancy probabilities and encode/decode features from finer levels, incorporating conditional coding and hyperprior models to reduce redundancy and bitstream size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional point cloud compression techniques are used, then compression is achieved, but computational requirements are too high for consumer devices and reconstruction accuracy deteriorates
Solution Approach 1:
The patent segments the point cloud into multiple octree levels and processes them hierarchically. Coarser levels are encoded first, then finer levels are processed with conditional coding that adapts to the coarser level's representation. This segmentation allows consumer devices to achieve accurate reconstruction by processing data in manageable hierarchical chunks rather than handling the entire point cloud at once.
Solution Approach 2:
The patent implements dynamic coding rates through conditional arithmetic coding, where the coding rate for each octree level is adaptively adjusted based on the representation quality of coarser levels. This dynamic rate allocation allows the system to optimize between compression efficiency and reconstruction accuracy in real-time, adapting to the computational capabilities of consumer devices without sacrificing measurement precision.
2Productivity
If lossy coding is applied to reduce bitstream size, then compression efficiency improves, but reconstruction accuracy deteriorates
Solution Approach 1:
The patent applies preliminary coding of coarser octree levels before processing finer levels. By establishing a baseline representation at coarser levels first, the system can then apply lossy coding at finer levels with the knowledge that coarser structures are already represented. This preliminary action ensures that essential geometric information is preserved while allowing aggressive compression at higher detail levels.
Solution Approach 2:
The patent implements feedback mechanisms where the coding rate and quantization parameters for finer octree levels are adjusted based on the representation quality and bitstream characteristics of coarser levels. This feedback loop allows the system to maintain reconstruction accuracy by adapting the lossy coding parameters dynamically, ensuring that compression efficiency gains do not compromise the essential geometric fidelity required for accurate reconstruction.
3Measurement precision
If neural network-based feature extraction is used, then reconstruction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the neural network feature extraction into hierarchical octree levels, applying network-based processing only where necessary. Coarser levels use simpler processing while finer levels employ more complex neural network feature extraction adaptively. This segmentation reduces overall computational complexity by avoiding unnecessary complex processing at coarser levels where simple geometric representation suffices.
Solution Approach 2:
The patent applies different levels of neural network processing to different octree levels based on local quality requirements. Finer octree levels that require higher reconstruction accuracy receive comprehensive neural network feature extraction, while coarser levels use simplified processing. This local quality approach ensures that computational resources are concentrated only where needed to maintain measurement precision.
Data Source
AI summary
In one implementation, geometry of a point cloud is encoded/decoded. On the encoder side, the encoder determines a first feature representing a voxel occupancy status of a current level and/or one or more finer levels of the point cloud, based on the voxel occupancy status of the current level and/or the finer levels; determines a second feature representing prediction of the voxel occupancy status of the current level and/or the finer levels of the point cloud; determines a third feature associated with the voxel occupancy status of the current level and/or the finer levels, based on the first feature and the second feature; and encodes the third feature. On the decoder side, the first feature is decoded from a bitstream, the second feature is determined similarly as the encoder side, and the third feature is determined based on the first feature and the second feature.