3D Point Cloud Compression via Distance-Based Bit Allocation
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Solution Overview
Problem
Point clouds, representing three-dimensional data, require efficient compression methods to reduce memory usage, as they occupy significant resources and need to be transformed into smaller capacity 2D data for effective processing.
Innovation Solution
A method and device for processing three-dimensional data by projecting points onto a plane, determining the number of bits representing projection points based on distance and position relationships, and varying bit allocation to represent relative positions between points, allowing for efficient transformation into two-dimensional data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is compressed into smaller capacity 2D data, then memory usage is reduced and transmission efficiency is improved, but data precision and reconstruction accuracy deteriorate
Solution Approach 1:
The patent applies local quality by differentiating bit allocation based on spatial position. Points closer to the projection plane are allocated more bits to preserve detail, while distant points receive fewer bits. This creates non-uniform data representation where critical regions maintain high precision while less critical regions are compressed more aggressively, resolving the contradiction between overall compression and local accuracy.
Solution Approach 2:
The patent changes the parameter of bit allocation dynamically based on distance metrics. By calculating the distance between points and the projection plane, the system adjusts the number of bits allocated to represent each point's position. This parameter change enables adaptive compression that maintains reconstruction accuracy for important points while reducing data capacity for less critical points.
2Device complexity
If uniform bit allocation is used for all projection points, then processing simplicity is maintained, but data transmission efficiency and storage optimization deteriorate
Solution Approach 1:
The patent implements dynamic bit allocation where the number of bits assigned to each projection point varies based on its distance from the projection plane. This dynamic approach replaces static uniform allocation, allowing the system to optimize data representation for each specific point while maintaining manageable processing complexity through systematic distance-based categorization.
Data Source
AI summary
A method and an apparatus for compressing the three-dimensional data of a point cloud are disclosed. The present invention comprises, identifying a plurality of points constituting a point cloud, and projecting the plurality of identified points onto a projection plane so as to generate a projection image, wherein the bit number of projection points corresponding to the plurality of points projected in the projection image is determined on the basis of the distance between a first point closest to the projection plane and a second point farthest therefrom, on a normal with respect to the projection plane, among the plurality of points and/or on the basis of the number of intermediate points located between the first point and the second point.