Hierarchical Search Tree for 3D Point Cloud Feature Extraction
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Solution Overview
Problem
Current computer vision methods face challenges in efficiently detecting and extracting features from large 3D point clouds, particularly in real-time object recognition, due to the computational intensity of calculating statistical measures for a large number of points.
Innovation Solution
A method involving the construction of a hierarchical search tree, where geometric information is stored in tree nodes, allowing for efficient calculation of statistical measures by representing points with the smallest number of nodes, thereby reducing the need for repeated calculations and improving processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point-based approaches are used to analyze large 3D point clouds, then comprehensive coverage of the scene is achieved, but computational intensity and processing time increase significantly
Solution Approach 1:
The patent segments the 3D point cloud into multiple features, where each feature represents a salient region or object of interest. This segmentation allows the system to process only the most relevant portions of the point cloud rather than all points, significantly reducing computational load while maintaining comprehensive scene understanding capability.
Solution Approach 2:
The patent extracts salient features from the large 3D point cloud by identifying and isolating significant regions. This extraction process filters out redundant information and focuses computation on the most important structural elements, enabling efficient processing while preserving essential scene characteristics.
2Productivity
If feature-based approaches are used to reduce computational load, then processing efficiency improves, but the ability to handle complex scenes with occlusions and clutter challenges increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing the point cloud to identify and extract salient features before main analysis. This preliminary feature extraction creates a simplified representation that maintains the essential characteristics needed for accurate object recognition, registration, and scene understanding while reducing subsequent computational complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms where the extracted features are continuously validated and refined during the analysis process. This feedback loop ensures that features maintain their salience and accuracy throughout processing, allowing the system to adapt to complex scene conditions including occlusions and clutter while maintaining high processing efficiency.
Data Source
AI summary
A method of object recognition and/or registration includes receiving a point cloud, arranging the points of the point cloud into a hierarchical search tree, and determining geometric information of the points located within a region, by identifying a highest level tree nodes where all of descendent leaf nodes are contained within the region and selecting the leaf nodes for the points where no sub-tree is entirely contained within the region, such that the points falling within the region are represented by the smallest number of nodes and performing statistical operations on the nodes representing the points in the region. The geometric information includes descriptors of features in the point cloud. The method further includes comparing the feature descriptors with a database of feature descriptors for a plurality of objects.