Point Cloud Compression Using Morton Index Differences
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Solution Overview
Problem
Current point cloud compression techniques face challenges in reducing computational effort and bitstream size, particularly in efficiently encoding and decoding 3D data structures like point clouds used in augmented and virtual reality applications.
Innovation Solution
A method combining 1-dimensional predictor-based Morton Index Differences with differential vector patterns, utilizing quantization, sequence sorting, and entropy coding to generate efficient codewords for point cloud data, which reduces bitstream size and computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If traditional point cloud compression techniques are used, then bitstream size can be reduced, but computational effort and complexity increase significantly
Solution Approach 1:
The point cloud data is divided into multiple patches or regions, allowing independent processing of each segment. This segmentation reduces the computational complexity of encoding while maintaining compression efficiency, as each patch can be processed separately with simpler algorithms.
Solution Approach 2:
The patent applies preliminary sorting of points based on depth or distance before compression. This pre-processing step organizes the data in a way that simplifies subsequent encoding operations, reducing the computational effort required during the actual compression phase while achieving effective bitstream reduction.
2Loss of substance
If complex compression algorithms are applied, then bitstream size decreases, but encoding time and latency increase
Solution Approach 1:
The patent extracts and utilizes the inherent spatial structure and regularities in point cloud data, such as surface continuity and geometric patterns. By leveraging these naturally occurring structures, the method achieves efficient compression without requiring complex algorithms, thus reducing encoding time and latency.
Solution Approach 2:
The compression method exploits the self-similar and self-organizing properties of point cloud data. The data's inherent geometric regularities serve the compression purpose directly, eliminating the need for elaborate external processing algorithms and reducing computational overhead and encoding time.
3Loss of substance
If high compression ratios are achieved, then bitstream size is reduced, but decoding complexity and computational effort increase
Solution Approach 1:
The patent applies different compression strategies to different regions of the point cloud based on local characteristics. Important geometric features receive more detailed encoding while less critical areas use simpler methods. This localized approach achieves high compression ratios without uniformly increasing decoding complexity across the entire dataset.
4Manufacturing precision
If detailed point cloud data is maintained, then quality is preserved, but data size and storage requirements increase
Solution Approach 1:
The patent applies compression selectively to different parts of the point cloud data based on their importance. Critical geometric features and visible surfaces maintain higher precision with minimal compression, while less important regions undergo more aggressive compression. This partial application of compression techniques preserves overall quality while achieving significant data size reduction.
Data Source
AI summary
A method for point cloud coding (200 or 300) or decoding (400 or 500) includes identifying (201) a plurality of quantized points in 3D space, defining (202), a sequence order of the quantized points in 3D space, and identifying (203) differential vector patterns in the sequence order of quantized points in 3D space. The method further includes identifying (204) Morton Index Differences in the sequence order of quantized points in 3d space, coding (205) the one or more quantized points in 3D space using differential vector patterns in a first plurality of codewords, coding (206) the one or more quantized points in 3D space using the Morton index differences in a second plurality of codewords, combining (207) the first and second plurality of codewords into a third plurality of codewords, and coding (208) the third plurality of codewords using an entropy coding scheme into a bitstream.