Point Cloud Geometry Encoding Using Cuboid Edge Context
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Solution Overview
Problem
Existing point cloud compression technologies are inefficient and lack versatility in handling diverse types of point clouds, particularly sparse and dense point clouds, leading to high data volumes and suboptimal compression performance.
Innovation Solution
The method and device utilize an entropy coder with improved contextual information to encode and decode geometrical information of point clouds represented by cuboid volumes, using presence flags and occupancy information of neighboring volumes and vertex positional information to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing point cloud compression technologies are used, then data can be compressed, but compression efficiency is low and data volume remains high
Solution Approach 1:
The patent implements feedback by using occupancy information from neighboring cuboid volumes and vertex positional information from already-coded neighboring edges to dynamically adjust the coding probability in the entropy coder. This feedback mechanism allows the encoder to adapt to local geometric characteristics, improving compression efficiency by better predicting the presence of vertices and reducing the number of bits required to encode geometrical information.
Solution Approach 2:
The patent changes the parameter of coding probability in the entropy coder based on contextual information from neighboring volumes and edges. By dynamically adjusting this parameter rather than using a fixed probability, the system optimizes compression for different regions of the point cloud, achieving better compression ratios while maintaining data accuracy.
2Reliability
If existing compression methods are applied, then point cloud data can be transmitted, but transmission quality is suboptimal
Solution Approach 1:
The patent optimizes transmission quality by dynamically changing the coding probability parameter in the entropy coder based on contextual information. This allows for more accurate representation of geometrical features while using fewer bits, thereby improving transmission quality without proportionally increasing data volume.
3Productivity
If conventional entropy coding is used, then encoding can be performed, but compression performance is suboptimal
Solution Approach 1:
The patent uses feedback from neighboring volume occupancy and edge vertex positions to adjust coding probability, improving compression performance. The complexity increase is managed by using only already-coded neighboring information, avoiding the need for complex global optimization algorithms.
Data Source
AI summary
A point cloud is represented by a plurality of cuboid volumes, and an occupied cuboid volume is modelled by one or more triangles. At least one triangle has at least one respective vertex on an edge of the occupied cuboid volume. The geometrical information including presence flags signaling a presence of a vertex. A method of encoding geometrical information of a geometry of a point cloud into a bitstream is includes for a current edge: constructing contextual information based on one or more or all of the following: occupancy information of neighboring cuboid volumes that abut the current edge, and vertex positional information of already-coded neighboring edges of the current edge, the neighboring edges being edges having a point in common with the current edge, using the contextual information to select a coding probability of an entropy coder, and encoding, by the entropy coder and using the selected coding probability, a presence flag for the current edge.