Regularized 2D Point Cloud Projection for Empty-Node Reduction
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Solution Overview
Problem
Existing point cloud encoding technologies, such as octree-based and prediction tree-based methods, fail to efficiently represent the spatial correlation of large-scale point clouds, leading to high ratios of empty nodes and inefficient encoding due to spatial sparsity, which hinders data processing, transmission, and storage.
Innovation Solution
A large-scale point cloud-oriented two-dimensional regularized planar projection method is employed, involving initialization of a two-dimensional projection plane structure using regularization parameters, and determining a mapping relationship between the original point cloud and the projection plane to enhance spatial correlation representation, followed by encoding and decoding based on this structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If octree-based geometric encoding is used, then the point cloud is divided into manageable nodes, but the spatial sparsity causes high ratios of empty nodes and poor representation of spatial correlation
Solution Approach 1:
The patent transforms the three-dimensional point cloud data into a two-dimensional projection plane structure. By projecting points from 3D space onto a 2D plane and establishing regularized correspondence relationships, the method eliminates empty nodes while preserving spatial correlation. This dimensional reduction converts the hierarchical octree structure into a planar grid structure where adjacent pixels in the projection plane correspond to spatially adjacent points in the original point cloud, enabling efficient prediction encoding.
2Adaptability or versatility
If prediction tree-based geometric encoding is used, then parameters from laser scanners are utilized to establish tree structure, but the spatial correlation of point cloud is not fully represented
Solution Approach 1:
The patent changes the fundamental parameters of the encoding structure by abandoning the hierarchical tree structure in favor of a two-dimensional projection plane with regularized pixel indexing. The projection plane uses standardized row and column indices instead of tree-based hierarchical coordinates, creating a uniform grid structure that better represents spatial relationships. This parameter transformation enables direct mapping between projection plane coordinates and original point cloud spatial positions.
3Productivity
If traditional point cloud encoding methods are used, then encoding can be performed, but encoding efficiency is not high enough due to high ratios of empty nodes
Solution Approach 1:
The patent extracts only the essential spatial information by projecting point cloud data onto a two-dimensional plane and removing redundant hierarchical structures. The projection process eliminates empty nodes by directly mapping visible points to projection plane pixels without creating intermediate hierarchical layers. This extraction approach retains only the necessary geometric information while discarding the octree hierarchy that generates empty nodes, significantly improving encoding efficiency.
4Loss of information
If large-scale point cloud data is processed, then comprehensive spatial information is obtained, but data volume is large and transmission and storage are difficult
Solution Approach 1:
The patent reduces data volume by transforming three-dimensional point cloud data into a two-dimensional projection plane representation. This dimensional reduction compresses the spatial information by eliminating redundant depth information while preserving essential spatial relationships through the projection mapping. The regularized pixel indexing in the 2D plane enables more efficient encoding and compression compared to the original 3D coordinate representation.
Data Source
AI summary
A large-scale point cloud-oriented two-dimensional regularized planar projection method and an encoding and decoding method are disclosed. The two-dimensional regularized planar projection method includes: obtaining original point cloud data; initializing a two-dimensional projection plane structure of a point cloud; and determining a mapping relationship between the original point cloud data and the two-dimensional projection plane structure to obtain a two-dimensional regularized projection plane structure corresponding to the point cloud. The encoding and decoding method includes: predicting based on the two-dimensional regularized projection plane structure of the point cloud to obtain to-be-encoded data; dividing the to-be-encoded data into a first type of to-be-encoded data and a second type of to-be-encoded data; and encoding different data in the first type of to-be-encoded data separately in a preset encoding manner, and correspondingly encoding the second type of to-be-encoded data, to obtain a geometric information bitstream.