Pre-segmenting Point Cloud Data for Real-Time Shape Extraction
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Solution Overview
Problem
Existing point cloud data processing methods fail to efficiently extract shapes and features in real-time due to the large volume of points, leading to computational intensity and inaccuracies, especially when dealing with massive datasets from laser scanners.
Innovation Solution
The method involves pre-segmenting point cloud data during the indexing process using an octree structure and storing segment information, allowing for the retrieval of only necessary points for real-time shape extraction, thereby reducing computational load and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all points in the point cloud are processed for shape extraction, then extraction accuracy is improved, but processing time increases and real-time performance is lost
Solution Approach 1:
The patent divides the point cloud data into multiple segments using an octree data structure during an indexing phase. Each node in the octree represents a spatial region and contains only the points within that region. This segmentation allows the system to process only relevant segments during shape extraction operations, rather than processing all points in the entire point cloud, thereby achieving real-time performance while maintaining accuracy.
Solution Approach 2:
The patent performs the segmentation and organization of point cloud data into an octree structure in advance, during an indexing phase that occurs before actual shape extraction operations. This preliminary action prepares the data in a optimized structure that enables fast querying and processing during real-time operations, resolving the contradiction between preparation time and operational speed.
2Productivity
If a subset of points is used for shape extraction, then processing speed is improved, but extraction accuracy deteriorates
Solution Approach 1:
The octree structure enables different levels of detail and point density to be maintained in different spatial regions. Each node in the octree can contain a different number of points based on the local complexity of the geometry in that region. This local quality approach ensures that regions requiring higher accuracy retain more points, while simpler regions use fewer points, optimizing both speed and accuracy.
3Ease of operation
If the entire point cloud is loaded into memory for processing, then complete data access is improved, but memory usage increases
Solution Approach 1:
The octree structure divides the point cloud into hierarchical segments that can be loaded and processed in manageable chunks. Each node in the octree represents a manageable subset of points that can fit in memory, allowing the system to process large point clouds that exceed available memory by working with segmented portions rather than requiring the entire dataset to be loaded at once.
Data Source
AI summary
A method, apparatus, system, and computer readable storage medium provide the ability to pre-segment point cloud data. Point cloud data is obtained and segmented. Based on the segment information, a determination is made regarding points needed for shape extraction. Needed points are fetched and used to extract shapes. The extracted shapes are used to cull points from the point cloud data.