Point Cloud Neighbor Point Selection via Weighted Geometry
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Solution Overview
Problem
The transmission of point cloud data is hindered by its large volume, requiring effective compression methods to improve encoding efficiency, particularly in selecting accurate neighbor points for attribute prediction.
Innovation Solution
A method and apparatus for selecting neighbor points in a point cloud by determining a target region, calculating weight coefficients, and choosing points based on geometry information to enhance the accuracy of attribute prediction and encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is compressed to reduce transmission volume, then transmission efficiency is improved, but the accuracy of attribute prediction deteriorates due to loss of geometric detail
Solution Approach 1:
The patent changes the parameter representation by introducing weight coefficients that modify how geometric distances are used in neighbor point selection. Instead of using raw Euclidean distances, the patent transforms distance parameters into weighted scores that better reflect actual attribute similarity, thereby maintaining prediction accuracy while working with compressed data representations.
Solution Approach 2:
The patent creates a composite selection criterion by combining multiple factors: geometric distance, weight coefficients derived from point distribution characteristics, and attribute similarity metrics. This composite approach allows the system to select neighbor points that are not just geometrically close but also attribute-wise similar, improving prediction accuracy despite data compression.
2Device complexity
If traditional Euclidean distance is used for neighbor point selection, then the selection process is simple, but the prediction accuracy deteriorates in regions with non-uniform point distribution
Solution Approach 1:
The patent applies local quality by introducing weight coefficients that are specific to different regions of the point cloud. Points in different spatial locations receive different weighting based on local density characteristics, allowing the selection process to adapt to local variations in point distribution rather than applying a uniform distance metric throughout the entire cloud.
Solution Approach 2:
The patent introduces dynamic weighting where the importance of different spatial dimensions varies based on local point distribution. The weight coefficients are calculated dynamically for each target point based on its specific geometric context, making the neighbor selection process adaptive rather than static.
Data Source
AI summary
This application provides a method for selecting a neighbor point of a current point in a point cloud. The method includes: determining, from point cloud data, a target region where the current point is located, the target region comprising a plurality of points; determining, for at least two decoded target points in the target region, a weight coefficient of each of the at least two target points, the at least two target points not comprising the current point; determining a weight of each of the at least two target points according to the weight coefficient and geometry information of each of the at least two target points and geometry information of the current point; and selecting at least one from the at least two target points according to the weight of each of the at least two target points as the neighbor point of the current point.