Multi-Resolution Motion Features for Dynamic Point Cloud Compression
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Solution Overview
Problem
Existing point cloud compression technologies face challenges in efficiently handling dynamic changes and variations in point cloud data, leading to suboptimal compression and reconstruction quality.
Innovation Solution
A multi-resolution motion feature generation process using neural network layers to enhance and merge motion features, followed by packing them into a bitstream for efficient compression and reconstruction of point clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multi-resolution motion features are generated and merged using multiple neural network layers, then compression efficiency and reconstruction quality are improved, but device complexity increases
Solution Approach 1:
The motion feature extraction process is segmented into multiple resolution levels (first resolution, second resolution, third resolution). Each resolution level is processed by separate neural network layers to extract motion features at different detail levels. This segmentation allows the system to capture both coarse and fine motion details independently, which are then merged to achieve high reconstruction quality without overwhelming a single processing stage.
Solution Approach 2:
Motion features extracted at different resolution levels are merged together through concatenation and processing by additional neural network layers. The first motion feature (from first resolution), second motion feature (from second resolution), and third motion feature (from third resolution) are combined to form a comprehensive multi-resolution motion feature representation. This merging integrates information from multiple scales, improving reconstruction quality while distributing computational load across multiple specialized processing stages.
2Measurement precision
If multiple motion features at different resolutions are extracted and merged, then dynamic changes in point cloud data are captured more effectively, but processing time increases
Solution Approach 1:
The processing is segmented into parallel resolution levels where each level processes motion features independently. The first set of neural network layers processes the first resolution, the second set processes the second resolution, and the third set processes the third resolution. This parallel segmentation allows dynamic changes to be detected at multiple scales simultaneously, improving detection accuracy while enabling potential parallel processing to mitigate time increases.
Solution Approach 2:
Motion features are extracted at multiple resolution levels before the final merging stage. By performing the extraction operations in advance at different resolutions, the system prepares comprehensive motion information that can be quickly merged and used for compression. This preliminary extraction at multiple scales ensures that when compression and reconstruction are performed, the dynamic changes are already well-characterized, reducing the need for additional processing time later.
Data Source
AI summary
Some embodiments of a method may include: obtaining a first motion feature generated by a first set of neural network layers with a current feature and a reference feature as inputs; obtaining a second motion feature generated by a second set of neural network layers with a downsampled current feature and a downsampled reference feature as inputs; generating a third motion feature by a third set of neural network layers by upsampling the second motion feature; generating a multi-resolution motion feature by a fourth set of neural network layers by merging the first and the third motion features; and packing the multi-resolution motion feature into a bitstream.