Regularized 2D Point Cloud Encoding for Lower Redundancy
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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 that projects three-dimensional data onto a two-dimensional regularization plane, utilizing a two-dimensional projection structure to enhance spatial correlation and reduce redundancy through encoding multiple image information maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If octree-based geometric encoding is used, then the point cloud data is processed through tree division, but the spatial correlation of point cloud cannot be fully reflected and encoding efficiency is low
Solution Approach 1:
The patent transforms the three-dimensional point cloud data into a two-dimensional regularized plane projection. This dimensional change allows the spatial correlation information to be better organized and reflected in a structured manner, improving both the reliability of spatial correlation representation and the productivity of encoding efficiency.
2Productivity
If prediction tree-based geometric encoding is used, then the tree structure is established based on laser scanner classification, but the spatial correlation of point cloud is not fully reflected and encoding efficiency is insufficient
Solution Approach 1:
The patent projects the three-dimensional point cloud onto a two-dimensional regularized plane, creating a new dimensional representation that better captures spatial correlations. This approach improves encoding efficiency by enabling more effective prediction and reduces the loss of spatial correlation information.
3Quantity of substance
If three-dimensional point cloud data is directly encoded, then the original spatial information is preserved, but the data amount is large and transmission and storage are not conducive
Solution Approach 1:
The patent extracts the essential spatial correlation information from the three-dimensional point cloud by projecting it onto a two-dimensional regularized plane. This extraction process reduces the data amount while preserving the critical spatial relationships, making transmission and storage more efficient.
Solution Approach 2:
By transforming the data from three-dimensional to two-dimensional representation, the patent reduces the quantity of data while maintaining spatial correlation information. This dimensional reduction directly improves transmission and storage efficiency without losing essential geometric information.
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.