Point Cloud Attribute Prediction Using Rate-Distortion Indexing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies face challenges in efficiently compressing and representing 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 3D scenes and objects.
Innovation Solution
The method involves processing point cloud data by determining neighboring points based on geometric distance, using a rate-distortion decision function to predict attribute values, and generating a most probable index list for efficient attribute prediction and compression, facilitating both lossy and lossless compression techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compression methods are used for point cloud data, then device complexity is reduced, but coding efficiency and compression ratio deteriorate
Solution Approach 1:
The patent segments the point cloud data into multiple octants based on the origin point, and further divides each octant into sub-regions. This segmentation enables independent processing of different spatial regions, improving coding efficiency while managing device complexity through localized operations rather than global processing.
Solution Approach 2:
The patent introduces a hierarchical dimension by organizing points into multiple levels (origin points, first-level neighboring points, second-level neighboring points). This multi-level hierarchical structure adds a dimensional layer to the compression process, enabling more efficient exploitation of spatial correlations without proportionally increasing device complexity.
2Measurement precision
If lossless compression is applied to point cloud data, then data accuracy is improved, but compression ratio deteriorates
Solution Approach 1:
The patent applies different compression strategies to different spatial regions and point types. Origin points and neighboring points are processed with different levels of detail and prediction methods. This local quality approach allows lossless compression for critical points while using more aggressive compression for less critical regions, balancing data accuracy and compression ratio.
Solution Approach 2:
The patent performs prediction and refinement operations selectively on specific point groups rather than all points. By applying partial action only where needed (e.g., refining neighboring points based on origin points), the method achieves lossless compression for essential data while avoiding the full computational overhead and storage cost of complete lossless compression.
3Measurement precision
If complex prediction algorithms are used for attribute prediction, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs prediction operations in a predetermined sequence: first predicting origin points, then using those predictions to refine neighboring points. This preliminary action approach ensures that predictions are made in an optimal order, improving accuracy through iterative refinement while managing processing time by avoiding redundant calculations.
Solution Approach 2:
The patent extracts and processes different point types (origin points vs. neighboring points) separately with specialized algorithms tailored to each type. By taking out and treating critical points with more complex prediction while using simpler methods for less critical points, the system achieves high prediction accuracy for important data without uniformly increasing processing time for all points.
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. A first index is received and the first index indicates a reconstructed attribute value selected from the reconstructed attribute values of the plurality of neighboring points. The reconstructed attributed value indicated by the first index is determined based on a rate-distortion decision function. An attribute distance for each of the neighboring points can be determined based on the reconstructed attribute values of the neighboring points.