Point Cloud Feature Extraction Using Segmentation and Transform Coefficients
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Solution Overview
Problem
Existing methods for feature extraction from point clouds require large memory and processing time due to the handling of a large number of data points with random spatial locations, leading to unreliable and slow processing speeds.
Innovation Solution
The method involves acquiring a point cloud, segmenting it into sub-volumes corresponding to objects, applying a non-uniform transform to obtain transform coefficients, and selecting a subset of these coefficients to extract uniform features, thereby reducing memory usage and enhancing processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If feature extraction is performed on the entire point cloud, then comprehensive feature information is obtained, but memory usage increases and processing speed decreases
Solution Approach 1:
The patent divides the point cloud into multiple sub-volumes (first sub-volume, second sub-volume, etc.) and processes each sub-volume separately. This segmentation reduces the number of data points that need to be processed simultaneously, thereby decreasing memory usage and improving processing speed while maintaining feature extraction reliability through subsequent fusion of results from all sub-volumes.
2Reliability
If feature extraction is performed on the entire point cloud, then comprehensive feature information is obtained, but memory resources are excessively consumed
Solution Approach 1:
The patent segments the point cloud into multiple sub-volumes and processes each sub-volume independently, which reduces the memory footprint at any given time. The results from all sub-volumes are then fused to reconstruct the complete feature representation, ensuring that comprehensive feature information is obtained without requiring excessive memory resources.
3Adaptability or versatility
If data points from different acquisition positions are used, then complete object coverage is achieved, but feature extraction becomes unreliable due to varying data point locations and counts
Solution Approach 1:
The patent divides the point cloud into multiple sub-volumes, where each sub-volume contains data points from a specific spatial region. This segmentation approach handles varying data point locations and counts by processing each sub-volume independently, then fusing the results to achieve reliable and complete object coverage regardless of acquisition position variations.
Data Source
AI summary
A method for extracting uniform features of at least one object, from a point cloud of an environment, includes acquiring the point cloud associated with the environment having the at least one object, wherein the point cloud is associated with a volume comprising a plurality of points; segmenting the point cloud into at least one sub-volume corresponding to each of the at least one object; applying a non-uniform transform on each of the plurality of points corresponding to each of the at least one sub-volume, to obtain a transform coefficient for each of the plurality of points; and selecting a subset of the plurality of transform coefficients as the extracted uniform features of the at least one object within the environment.