Hybrid Coding Order for Point Cloud Octree Compression
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Solution Overview
Problem
Current point cloud compression technologies face challenges in efficiently compressing and decompressing large datasets for real-time applications, such as autonomous driving and virtual reality, while maintaining high quality and handling dynamic changes in point clouds.
Innovation Solution
The implementation of a hybrid coding order technique for point cloud compression, which combines breadth-first and depth-first coding orders within an octree structure, allowing for parallel processing and efficient decoding of occupancy codes, and the use of existing video codecs to compress geometry, occupancy, and texture as separate video sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compression methods are used for point cloud data, then compression is achieved, but decompression speed is insufficient for real-time applications
Solution Approach 1:
The patent divides the point cloud into an octree structure with multiple nodes of different sizes, allowing selective decoding of occupied nodes. This segmentation enables the decoder to process only relevant portions of the point cloud, significantly improving decompression speed for real-time applications while maintaining compression efficiency.
Solution Approach 2:
The patent implements dynamic coding order selection where the decoder can choose between breadth-first and depth-first traversal orders based on the specific node occupancy patterns. This dynamic adaptation allows optimal decoding performance for different point cloud configurations, resolving the contradiction between compression efficiency and decompression speed.
2Quantity of substance
If large point cloud datasets are processed, then comprehensive 3D representation is achieved, but storage and transmission costs increase
Solution Approach 1:
The patent extracts and encodes only the occupancy information of non-empty nodes in the octree structure, rather than processing the entire point cloud uniformly. This extraction approach significantly reduces the amount of data that needs to be stored and transmitted, while still maintaining complete representation of the actual point cloud content.
Solution Approach 2:
The patent applies different coding strategies to different nodes based on their occupancy status and size. Occupied nodes are encoded with detailed information while empty nodes are represented by simple occupancy flags, creating a non-uniform coding scheme that optimizes the balance between data quantity and storage/transmission requirements.
3Device complexity
If sequential decoding of occupancy codes is used, then simple decoding process is maintained, but processing time increases
Solution Approach 1:
The patent performs preliminary organization of the point cloud into an octree structure with pre-established node relationships before decoding begins. This preliminary structuring enables parallel decoding operations during the actual decoding process, reducing processing time while maintaining relatively simple decoding logic through the use of standard traversal algorithms.
Data Source
AI summary
An apparatus for point cloud coding, includes processing circuitry that receives a coded bitstream for a point cloud. The coded bitstream includes encoded data for nodes in an octree structure for the point cloud corresponding to three dimensional (3D) partitions of a space of the point cloud, node sizes of the nodes being associated with sizes of the corresponding 3D partitions of the nodes. The processing circuitry decodes, from the coded bitstream, a first set of occupancy codes for a first set of nodes in the nodes using a first coding order and a second set of occupancy codes for a second set of nodes in the nodes using a second coding order that is different from the first coding order. Further, the processing circuitry reconstructs the octree structure based on at least the first set of occupancy codes and the second set of occupancy codes.