Regularized 2D Plane Projection for Point Cloud Encoding Efficiency
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Solution Overview
Problem
Existing point cloud encoding technologies, such as octree-based and prediction tree-based methods, fail to fully reflect the spatial correlation of point clouds, leading to inefficient encoding due to large empty nodes and insufficient entropy encoding.
Innovation Solution
A point cloud encoding method using a two-dimensional regularization plane projection to project and encode geometry and attribute information, including placeholder, depth, projection residual, and coordinate conversion error maps, to enhance spatial correlation representation and reduce redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If octree-based geometric encoding and decoding is used, then the point cloud can be divided into hierarchical structures, but the spatial correlation of the point cloud cannot be fully reflected and a relatively large proportion of empty nodes are obtained
Solution Approach 1:
The patent transforms the three-dimensional point cloud data into a two-dimensional projection plane structure by performing projection along the Z-axis. This dimensionality reduction reorganizes the spatial distribution of points, eliminating the hierarchical octree structure and its associated empty nodes while preserving and enhancing spatial correlation through the projected two-dimensional layout.
2Adaptability or versatility
If prediction tree-based geometric encoding and decoding is used, then the tree structure can be established according to laser scanners, but the spatial correlation of the point cloud is not fully reflected
Solution Approach 1:
The patent projects three-dimensional point cloud data onto a two-dimensional plane, fundamentally changing the data organization from tree-structured hierarchical predictions to a spatially-correlated projection structure. This transformation maintains adaptability through flexible projection parameters while significantly improving spatial correlation representation by preserving the actual spatial relationships between points in the projected domain.
3Productivity
If traditional point cloud encoding methods are used, then the encoding process can be implemented, but the encoding efficiency is insufficient due to large amount of data and spatial sparsity
Solution Approach 1:
The patent achieves encoding efficiency improvement by projecting three-dimensional point cloud data onto a two-dimensional plane. This transformation reduces spatial redundancy by eliminating duplicate representations across multiple octree levels or prediction tree branches, while the projected structure maintains essential spatial information. The compact two-dimensional representation enables more efficient entropy encoding and reduces the overall data volume requiring transmission and storage.
Data Source
AI summary
Disclosed are a point cloud encoding and decoding method and device based on a two-dimensional regularization plane projection. The encoding method includes: acquiring original point cloud data; performing two-dimensional regularization plane projection on the original point cloud data to obtain a two-dimensional projection plane structure; obtaining a plurality of pieces of two-dimensional image information according to the two-dimensional projection plane structure; and encoding the plurality of pieces of two-dimensional image information to obtain code stream information.