Point Cloud Video Compression with Structure-Preserving Sampling
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Solution Overview
Problem
Existing learning-based point cloud geometry compression methods suffer from redundancy, structural irrationality, and limited reconstruction quality due to homogeneous point clouds, inadequate feature processing, and constrained up-sampling methods.
Innovation Solution
Implement down-sampling based on point importance, structure-preserving information, and reprocessing of features using neural networks to improve compression efficiency and accuracy, followed by progressive upsampling with unbalanced loss constraints and residual learning to enhance reconstruction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional uniform down-sampling is applied to point clouds, then processing complexity is reduced, but reconstruction quality deteriorates due to loss of important geometric features
Solution Approach 1:
The patent applies different down-sampling strategies to different regions of the point cloud based on their geometric importance. Critical regions with complex geometry use structure-preserving down-sampling to maintain features, while less important regions use simpler down-sampling methods, thus balancing processing complexity and reconstruction quality across different areas.
Solution Approach 2:
The patent dynamically adjusts down-sampling parameters based on local geometric characteristics. By computing geometric descriptors and importance metrics for different regions, the system adapts the down-sampling rate and method to preserve critical features while reducing overall complexity, resolving the contradiction between processing efficiency and reconstruction fidelity.
2Quantity of substance
If aggressive down-sampling is used to reduce data size, then compression ratio is improved, but geometric structure preservation deteriorates
Solution Approach 1:
The patent identifies and protects critical geometric structures by computing local geometric descriptors and importance metrics. Down-sampling is applied selectively, with higher rates in less important regions and lower rates in regions containing critical geometric features, thus achieving compression while preserving essential structure.
Solution Approach 2:
The patent introduces geometric descriptors and importance metrics as intermediary representations that guide the down-sampling process. These intermediaries encode structural information that informs the down-sampling strategy, allowing the system to compress data while maintaining geometric fidelity through the guidance of these intermediate representations.
3Measurement precision
If standard up-sampling methods are applied to reconstructed point clouds, then resolution is improved, but artifact introduction increases due to loss of fine geometric details
Solution Approach 1:
The patent performs preliminary processing of geometric features before down-sampling and uses this information to guide the up-sampling reconstruction process. By pre-computing and preserving key geometric descriptors, the system can reconstruct high-resolution point clouds that faithfully reproduce original geometric details without introducing artifacts from lost fine structures.
Solution Approach 2:
The patent employs feedback mechanisms where the down-sampled and reconstructed point clouds are compared against the original, and the differences are used to refine the reconstruction process. This feedback loop allows the system to identify and correct artifact introduction during up-sampling, improving resolution while maintaining geometric fidelity.
Data Source
AI summary
Embodiments of the disclosure provide a solution for video processing. A method for video processing is proposed. The method includes: applying, for a conversion between a target frame of a point cloud sequence and a bitstream of the point cloud sequence, down-sampling on points in the target frame according to importance of the points; obtaining a final sampled point could by combining a plurality of down-sampled points, wherein a first set of down-sampled points is down-sampled based on importance, and a second set of down-sampled points is down-sampled based on structure-preserving information; and performing the conversion based on the final sampled point cloud.