Dynamic Point Cloud Attribute Coding with Bottom-Up Octree Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing learning-based methods for octree-based attribute coding of point clouds suffer from inaccurate probability estimation and high complexity due to reliance on parent or sibling octree voxel information, lacking access to finer details.
Innovation Solution
A bottom-up strategy for point cloud attribute coding that estimates attribute probability distribution using finer octree levels, encoding features from child voxels to enhance accuracy and efficiency, combined with hyperprior encoding for hierarchical lossless and lossy compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If learning-based methods use parent or sibling octree voxel information for attribute coding, then the coding process can be simplified, but the probability estimation accuracy deteriorates due to lack of access to finer details
Solution Approach 1:
The patent inverts the traditional top-down approach by implementing a bottom-up strategy where child voxels encode their attribute information first, then parent voxels use this finer detail information to improve their probability estimation. This inversion allows finer level details to flow upward through the octree hierarchy, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The patent introduces a new dimension to the octree coding hierarchy by enabling bidirectional communication between parent and child voxels. Child voxels not only encode their own attributes but also transmit this information to parent voxels, creating a multi-dimensional information flow that improves probability estimation accuracy while maintaining manageable complexity through structured propagation.
2Manufacturing precision
If existing learning-based methods rely on limited parent or sibling voxel information, then the computational requirements are reduced, but the reconstruction quality deteriorates
Solution Approach 1:
The patent segments the octree into multiple levels with distinct coding stages. Each level processes and encodes attribute information independently using bottom-up strategies, allowing reconstruction quality to improve at each level without requiring all computations to be performed simultaneously. This segmentation enables progressive reconstruction that balances quality and computational resources.
Solution Approach 2:
The patent performs preliminary encoding of child voxel attributes before using this information for parent voxel probability estimation. This preliminary action allows the system to prepare and store fine-detail information that will be used in subsequent reconstruction stages, improving reconstruction quality while managing computational complexity through staged processing.
3Measurement precision
If a bottom-up strategy using finer octree levels is implemented, then probability estimation accuracy is improved, but the encoding process becomes more complex
Solution Approach 1:
The patent implements feedback mechanisms where child voxels provide attribute information back to parent voxels through the octree hierarchy. This feedback loop allows parent voxels to refine their probability estimates using actual child level data, improving accuracy while the structured feedback protocol maintains encoding simplicity through systematic information propagation.
Data Source
AI summary
In one implementation, a method for reconstructing attributes of a current point cloud frame from a sequence of point cloud frames is provided wherein a predicted feature map from attributes of a reference point cloud frame is obtained, a residual feature map is decoded from a bitstream, a feature map is reconstructed that represents voxel attributes at a current level in an octree structure of the current point cloud frame, from the decoded residual feature map and the predicted feature map, and voxel attributes are reconstructed at the current level in the octree structure based on the reconstructed feature map, wherein a reconstructed feature in the reconstructed feature map for a current voxel is representative of at least a set of voxels that are still to be reconstructed.