3D Point Cloud Object Verification for Ambiguous Image Regions
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Solution Overview
Problem
Existing methods struggle to accurately specify regions of detection target objects in two-dimensional images due to the loss of depth information, leading to incorrect identification of objects with different three-dimensional forms, such as cylinders and cubes.
Innovation Solution
A point cloud data processing apparatus and method that utilizes three-dimensional point cloud data to acquire and compare form information, allowing for accurate determination of object regions by associating image pixels with point data, and employing techniques like Hough transformation and RANSAC for feature extraction and verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If two-dimensional image information is used to specify object regions, then the specification process is simple, but depth information is lost making it difficult to accurately distinguish objects with different three-dimensional forms
Solution Approach 1:
The patent associates two-dimensional image pixel positions with three-dimensional point cloud data to recover depth information. By mapping 2D image coordinates to 3D spatial coordinates through point cloud association, the system enables accurate specification of object regions while distinguishing objects with different three-dimensional forms, thus resolving the contradiction between operational simplicity and information completeness.
2Productivity
If only two-dimensional image data is used for object detection, then the detection process is fast, but objects with similar two-dimensional projections but different three-dimensional forms cannot be distinguished
Solution Approach 1:
The patent extracts form information from three-dimensional point cloud data associated with detected object regions. By analyzing 3D geometric characteristics such as curvature, volume, and surface properties, the system can distinguish objects with different three-dimensional forms even when their two-dimensional projections are similar, thereby improving measurement precision without significantly compromising detection speed.
3Measurement precision
If three-dimensional point cloud data is used to verify object specification, then identification accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent divides the verification process into modular components: form information extraction from point cloud data, comparison with reference form information, and determination of specification accuracy. This segmentation allows the system to handle complex three-dimensional verification tasks through manageable processing stages, reducing overall system complexity while maintaining high identification accuracy.
Solution Approach 2:
The patent uses form information as an intermediary between the detected object region and the final determination of specification accuracy. By extracting and comparing form characteristics (such as geometric parameters and shape features) as intermediate data, the system simplifies the verification process and reduces computational complexity while ensuring accurate identification.
Data Source
AI summary
A point cloud data processing apparatus 11 includes a processor configured to acquire first form information that indicates a feature of a form of a first object, specify an object region of a second object that is identified from an image and that corresponds to the first form information, select second-object point cloud data, in point cloud data, that corresponds to the object region, on the basis of the object region, acquire second form information that indicates a feature of a form of the second object, on the basis of the second-object point cloud data, and compare the first form information with the second form information and perform determination as to whether the second object is the first object.