Point Cloud Projection Encoding with 2D Regularized Planes
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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
Project the point cloud onto a two-dimensional regularization plane structure, performing regularization corrections to enhance spatial correlation, and utilize this projection for improved entropy encoding of geometry and attribute information.
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 the point cloud cannot be fully reflected due to large proportion of empty nodes
Solution Approach 1:
The patent transforms the three-dimensional point cloud data into a two-dimensional regular array structure through projection. This dimensional change allows the data to be organized in a grid format where spatial correlation is preserved and can be effectively utilized for prediction and encoding, eliminating the empty node problem inherent in octree structures.
Solution Approach 2:
The patent changes the data structure parameter from a hierarchical tree structure (octree) to a regular two-dimensional array structure. This parameter change enables systematic organization of point cloud data while maintaining spatial correlation information, thereby improving both encoding efficiency and correlation reflection.
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 the point cloud is not fully reflected
Solution Approach 1:
The patent projects the three-dimensional point cloud onto a two-dimensional regular array, creating a structured grid that preserves spatial relationships. This dimensional transformation enables the data to be processed in a way that fully captures spatial correlation, overcoming the limitations of prediction tree structures that rely on laser scanner classification.
Solution Approach 2:
The patent changes the organizational parameter from laser scanner-based classification to a regular two-dimensional array structure. This parameter change allows for systematic arrangement of points while maintaining their spatial correlation, thereby improving encoding efficiency without sacrificing correlation reflection.
3Reliability
If three-dimensional point cloud data is used directly, then the spatial correlation is preserved, but the data amount is large and not conducive to transmission and storage
Solution Approach 1:
The patent extracts the essential spatial correlation information from the three-dimensional point cloud by projecting it onto a two-dimensional regular array. This extraction process retains the critical spatial relationship data while reducing the overall data volume, making it more suitable for transmission and storage while preserving the necessary spatial correlation for encoding.
Solution Approach 2:
By transforming the data from three-dimensional to two-dimensional representation, the patent reduces the data quantity while maintaining spatial correlation. The regular array structure provides a compact representation that captures spatial relationships with fewer data elements, thereby reducing storage and transmission requirements.
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, a point cloud in a three-dimensional space is projected to a corresponding two-dimensional regularization projection plane structure, and regularization correction is performed on the point cloud in a vertical direction and a horizontal direction, to obtain a strong correlation representation of the point cloud on the two-dimensional projection plane structure, so that the spatial correlation of the point cloud is better reflected; and when the plurality of pieces of two-dimensional image information are subsequently encoded, the spatial correlation of the point cloud can be greatly utilized, and the spatial redundancy is reduced, thereby further improving the encoding efficiency of the point cloud.