Point Cloud Encoding via 2D Regularization Plane Projection
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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 high proportions of empty nodes and inadequate spatial correlation representation.
Innovation Solution
Projecting point clouds onto a two-dimensional regularization plane, performing regularization correction, and utilizing placeholder and depth information maps to encode projection residual information, thereby enhancing spatial correlation representation and reducing redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If octree-based geometric encoding is used, then the point cloud can be divided into hierarchical structures, but the spatial correlation of the point cloud cannot be fully reflected leading to large proportion of empty nodes
Solution Approach 1:
The patent transforms the three-dimensional point cloud data into a two-dimensional projection plane structure. By projecting 3D coordinates (x, y, z) onto a 2D plane and establishing a corresponding relationship between 3D points and 2D pixels, the method enables more effective spatial correlation representation while reducing the complexity of hierarchical tree structures and minimizing empty nodes in the encoding process.
2Ease of operation
If prediction tree-based geometric encoding is used, then the point cloud can be organized by laser scanner parameters, but the spatial correlation is still insufficient for efficient prediction and entropy encoding
Solution Approach 1:
The patent projects the point cloud onto a two-dimensional regularization plane, creating a 2D projection plane structure that maintains spatial relationships more effectively than tree-based methods. This dimensional transformation enables better utilization of spatial correlation for prediction and entropy encoding, significantly improving encoding efficiency while preserving the organizational structure benefits.
3Loss of information
If traditional 3D point cloud encoding methods are used, then the geometry information can be encoded separately, but the data amount remains large不利于 transmission and storage
Solution Approach 1:
The patent reduces the data volume by projecting 3D point cloud data onto a 2D projection plane. The encoding process focuses on representing the 2D projection information and the correspondence relationships, which requires fewer bits than encoding full 3D geometry information while preserving the essential spatial structure and geometric characteristics of the original point cloud.
Data Source
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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. According to the present invention, the strong correlation representation of a point cloud on the two-dimensional projection plane structure is obtained by using the two-dimensional regularization plane projection technology, so that the spatial correlation of the point cloud is better reflected, and when a projection residual information map obtained for the two-dimensional regularization projection plane structure is subsequently encoded, the spatial correlation of the point cloud can be greatly utilized, thereby improving the encoding efficiency of the point cloud. Moreover, a placeholder information map and a depth information map are used for assisting in encoding the projection residual information map, so that the encoding efficiency is further improved.