Point Cloud Attribute Prediction Using Octree Segmentation
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Solution Overview
Problem
Current technologies face challenges in efficiently compressing and representing three-dimensional (3D) point cloud data, particularly in immersive applications like virtual reality and autonomous driving, where lossy and lossless compression, scalable coding, and random access are required for geometry and attribute data.
Innovation Solution
The method involves quantizing point cloud coordinates, generating an octree structure for geometry coding, performing attribute transfer, and using attribute prediction techniques based on neighboring points to reduce data complexity, enabling efficient compression and decompression of point cloud data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is compressed to reduce data size, then transmission efficiency is improved, but reconstruction accuracy deteriorates
Solution Approach 1:
The point cloud data is segmented into octree structures that hierarchically divide the 3D space into manageable regions. This segmentation allows selective compression of different spatial regions based on their importance, maintaining reconstruction accuracy for critical areas while achieving overall data reduction for improved transmission efficiency.
Solution Approach 2:
The patent applies attribute prediction techniques that transform the representation of point cloud attributes by predicting values based on neighboring points. This parameter transformation reduces the amount of data that needs to be transmitted while maintaining reconstruction accuracy, as only the prediction residuals need to be encoded rather than full attribute values.
2Productivity
If attribute prediction based on neighboring points is used, then compression ratio is improved, but computational complexity deteriorates
Solution Approach 1:
The patent performs preliminary organization of point cloud data into octree structures and identifies neighboring points before the actual attribute prediction process. This preliminary action pre-processes the data to make the subsequent prediction computations more efficient, reducing the overall computational complexity while maintaining high compression ratios.
Solution Approach 2:
The attribute prediction process creates simplified copies of attribute information from neighboring points rather than transmitting full attribute data. This copying approach achieves high compression ratios by replacing redundant attribute values with references to predicted values, while the computational complexity is managed through efficient neighbor identification algorithms.
3Productivity
If octree structure is generated for geometry coding, then data organization efficiency is improved, but processing time deteriorates
Solution Approach 1:
The octree structure segments the 3D point cloud space into hierarchical regions, dramatically improving data organization efficiency by grouping spatially related points together. This segmentation allows for efficient memory access patterns and localized processing, which mitigates the initial processing time required to build the structure.
Solution Approach 2:
The octree structure is generated as a preliminary organization step before attribute prediction and compression operations. By performing this data organization in advance, the patent enables subsequent processing steps to operate on pre-organized data structures, reducing overall processing time despite the initial overhead of building the octree.
Data Source
AI summary
A method of processing point cloud data at a decoder can include receiving three dimensional (3D) coordinates of a set of points of a point cloud including first points and a current point. Each of the first points can be associated with a reconstructed attribute value. A group of neighboring points of the current point can be determined from the first points. An attribute distance for each of the neighboring points can be determined based on the reconstructed attribute values of the neighboring points. An attribute prediction of the current point can be determined based on the attribute distances of the neighboring points.