Object Recognition via 3D Point Cloud Clustering and Cross-Referencing
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Solution Overview
Problem
Current object recognition techniques struggle to accurately identify and provide detailed information about arbitrary objects in scenes, especially under conditions of varying illumination, occlusion, and clutter, due to limitations in image quality and the lack of mathematical context.
Innovation Solution
The method involves generating 2D image information from overlapping images of a scene, combining it with 3D information to create projective geometry, and then clustering 3D data points to extract measurement, geometric, and topological information about objects, which is validated through cross-referencing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional computer-based object recognition methods are used, then the system can identify well-known objects with satisfactory results, but it fails to accurately recognize arbitrary objects under varying illumination, occlusion, and clutter conditions
Solution Approach 1:
The patent transitions from 2D image analysis to 3D point cloud processing to capture objects from multiple viewpoints and depths, enabling robust recognition under varying illumination, occlusion, and clutter conditions by adding spatial dimensionality to the recognition process
Solution Approach 2:
The system segments the scene into multiple point clouds representing different objects and surfaces, allowing individual object analysis independent of background clutter and occlusions, thereby improving recognition reliability for arbitrary objects
2Measurement precision
If 2D image information alone is used for object recognition, then the processing is simpler, but the measurement precision and geometric information extraction are insufficient
Solution Approach 1:
The patent employs 3D point cloud data in addition to 2D images, leveraging the extra spatial dimension to extract precise measurements and geometric properties while maintaining processing feasibility through systematic point cloud manipulation and analysis
Solution Approach 2:
The system combines 2D image information with 3D point cloud data to achieve both accurate measurements and geometric extraction, merging the simplicity of 2D processing with the precision of 3D spatial information
3Adaptability or versatility
If machine learning algorithms with positive and negative training are used, then the system can attempt to identify arbitrary objects, but the quality of recognition remains limited when object appearance deviates from canonical forms
Solution Approach 1:
By processing objects in 3D point cloud space rather than 2D images, the system captures invariant geometric properties that remain consistent across different viewpoints, illuminations, and occlusions, enabling accurate recognition of arbitrary objects regardless of appearance variations
Data Source
AI summary
Examples of various method and systems are provided for information extraction from scene information. 2D image information can be generated from 2D images of the scene that are overlapping at least part of one or more object(s). The 2D image information can be combined with 3D information about the scene incorporating at least part of the object(s) to generate projective geometry information. Clustered 3D information associated with the object(s) can be generated by partitioning and grouping 3D data points present in the 3D information. The clustered 3D information can be used to provide, e.g., measurement information, dimensions, geometric information, and/or topological information about the object(s). Segmented 2D information can also be generated from the 2D image information. Validated 2D and 3D information can be produced by cross-referencing between the projective geometry information, clustered 3D information, and/or segmented 2D image information, and used to label the object(s) in the scene.