Point Cloud Color Smoothing via Selective Filtering
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Solution Overview
Problem
Current point cloud compression technologies face challenges in efficiently compressing 3D representations for real-time communications and applications like virtual reality, dynamic mapping for autonomous driving, and cultural heritage, particularly in handling geometry, attributes, and metadata, with existing methods being computationally expensive and requiring significant memory.
Innovation Solution
The implementation of video-based point cloud compression (V-PCC) methods that leverage existing video codecs to compress geometry, occupancy, and texture as separate video sequences, using filtering techniques like bilateral and trilinear filters to reduce data volume and improve compression efficiency, while also addressing computational complexity and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If filtering techniques are applied to reduce data volume, then compression efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent applies bilateral filtering and trilinear filtering with adjustable parameters (filtering strength, spatial range, attribute differences) to balance compression efficiency and computational complexity. By changing filtering parameters based on local characteristics, the system achieves better compression without uniformly high computational cost across all regions.
Solution Approach 2:
The filtering is applied selectively based on local attribute differences and spatial criteria. Regions with significant attribute variations use stronger filtering, while homogeneous regions use lighter filtering, optimizing the balance between compression efficiency and computational effort in different local areas.
2Manufacturing precision
If filtering is applied to smooth color attributes, then visual quality is improved, but processing time increases
Solution Approach 1:
Instead of applying filtering uniformly to all points, the patent applies filtering partially based on whether points meet specific criteria (attribute differences, spatial relationships). This selective approach improves visual quality where needed while minimizing processing time in regions where filtering is unnecessary.
Solution Approach 2:
The patent performs preliminary calculations of attribute differences and spatial criteria before applying filtering. By pre-evaluating which points require filtering and determining appropriate filter strengths, the system avoids unnecessary processing and reduces overall processing time while maintaining visual quality.
3Measurement precision
If maximum difference values are calculated for all attribute sets, then filtering accuracy is improved, but memory requirements increase
Solution Approach 1:
The patent extracts only the necessary maximum difference values for the specific attribute sets required for filtering decisions, rather than calculating and storing all possible attribute statistics. This selective extraction maintains filtering accuracy while reducing memory requirements by keeping only essential data.
Data Source
AI summary
Systems and methods for decoding a video stream by at least one processor, a method includes receiving the video stream, the video stream including at least one cell; and reconstructing a cell from among the at least one cell. The reconstructing includes: obtaining a maximum difference value for each of at least one set of attribute values of the cell; and determining whether to apply a filter to the cell based on the maximum difference value of a set of attribute values of the at least one set of attribute values.