3D Point Cloud Occupancy Coding With Raster-Scan Neighbors
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Solution Overview
Problem
Existing point cloud compression methods, such as MPEG-GPCC and MPEG-VPCC, face inefficiencies in compressing sparse and dynamic point clouds, particularly in terms of memory usage and complexity, especially when encoding and decoding occupancy trees, due to the reliance on occupancy atlases and inefficient neighbor search algorithms.
Innovation Solution
The method employs a raster scan order for encoding and decoding 3D point clouds using an octree structure, where nodes are processed in a lexicographic order, determining neighbor patterns based on a subset of neighbor nodes, and entropy encoding/decoding occupancy information without the need for a full occupancy atlas, reducing memory usage and complexity to O(N).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If occupancy atlases and inefficient neighbor search algorithms are used in existing point cloud compression methods, then compression capability is achieved, but memory usage and algorithm complexity increase significantly
Solution Approach 1:
The patent segments the occupancy tree traversal into depth-based layers, processing nodes at each depth level before moving to the next depth. This segmentation eliminates the need for large occupancy atlases by only maintaining neighbor information for the current depth level, reducing memory complexity from O(2^d) to O(N) where N is the number of nodes at current depth.
Solution Approach 2:
The patent changes the neighbor search from a spatial 3D coordinate-based approach to a depth-level and index-based approach. By organizing nodes in raster scan order within each depth level and using index arithmetic to identify neighbors, the solution transforms the complex spatial search into a simple linear index operation, reducing algorithmic complexity.
2Productivity
If raster scan order with lexicographic ordering is used for encoding, then compression efficiency improves, but processing order complexity increases
Solution Approach 1:
The patent pre-establishes the raster scan ordering scheme and neighbor index relationships before the actual encoding process. By defining the lexicographic ordering rules and neighbor offset calculations in advance, the complex processing order is resolved beforehand, allowing simple index arithmetic during encoding without real-time complexity.
3Reliability
If full occupancy atlas is maintained for neighbor search, then accurate neighbor information is available, but memory consumption increases
Solution Approach 1:
The patent extracts only the essential neighbor information needed for entropy coding from the full occupancy atlas concept. Instead of maintaining complete occupancy data for all possible nodes, it extracts and maintains only the neighbor relationships within the current depth level, achieving accurate neighbor information with minimal memory consumption.
Data Source
AI summary
A method for encoding a 3D point cloud into a bitstream is implemented in an encoder, a geometry of the point cloud being defined in an octree-based structure having a plurality of nodes having parent-child relationships and representing a three-dimensional location of an object, the point cloud being located within a volumetric space of a three-dimensional coordinate system recursively split into sub-volumes and containing points of the point cloud, wherein a volume is partitioned into a set of sub-volumes, each associated with a node of the octree-based structure. The method includes: obtaining nodes encompassing at least part of the point cloud at a depth d in a lexicographic order along each axis of the coordinate system; determining a neighbour pattern for each node Nk based on a subset of neighbour nodes; and entropy encoding the occupancy information of each node into a bitstream based on the neighbour pattern.