Edge Detection via 3D Spatial Validation
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Solution Overview
Problem
Existing technologies face challenges in accurately identifying and segmenting object edges from spatial structure information and 2D image data, particularly in distinguishing between physical edges and false edges.
Innovation Solution
A computing system and method that processes spatial structure information and 2D image information by identifying 3D locations representing object corners, determining corresponding 2D locations, and filtering out clusters of candidate edges that do not match the orientation of the 2D vector, thereby isolating genuine 2D object edges for segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional edge detection methods are used on 2D image data, then all candidate edges are identified, but false edges cannot be distinguished from physical edges
Solution Approach 1:
The method segments the edge detection process into multiple stages: first identifying all candidate edges from 2D image data, then filtering them by comparing with 3D spatial structure information. This segmentation allows differentiation between false edges and physical edges through multi-source data validation.
Solution Approach 2:
The patent uses 3D spatial structure information as an intermediary to validate 2D edge candidates. By introducing this intermediate verification layer, the system can distinguish false edges from real edges without losing valuable edge information.
2Measurement precision
If 3D spatial structure information is integrated with 2D image data, then edge detection accuracy improves, but processing complexity increases
Solution Approach 1:
The method performs preliminary processing by identifying candidate edges from 2D image data before validating them against 3D spatial structure information. This preliminary action reduces the complexity of subsequent 3D-2D matching by pre-filtering obvious candidates.
Solution Approach 2:
The patent applies different processing qualities to different edge candidates: simple 2D detection for initial candidate identification, and more complex 3D-2D correspondence validation only for promising candidates. This local quality approach optimizes overall processing efficiency.
3Manufacturing precision
If all candidate edges are processed for segmentation, then comprehensive object segmentation is achieved, but processing time increases due to false edges
Solution Approach 1:
The method extracts and removes false edges from the candidate set by comparing 2D edge candidates with 3D spatial structure information. This extraction of harmful elements (false edges) prevents them from degrading segmentation quality and reduces unnecessary processing.
Solution Approach 2:
The patent applies full 3D-2D validation selectively to candidate edges that meet initial 2D detection criteria, rather than validating all possible edges. This partial action approach maintains segmentation precision while improving processing efficiency.
Data Source
AI summary
A system and method for processing candidate edges are presented. The method may be performed when spatial structure information and 2D image information are stored. The method may include identifying, based on the spatial structure information, a 3D location that represents a corner of an object structure; identifying, based on the spatial structure information, a 3D vector that extends from the 3D location and is parallel with an edge; determining a 2D location within the 2D image information that corresponds to the 3D location; determining a 2D vector within the 2D image information that corresponds to the 3D vector; determining an edge detection region; identifying a cluster of candidate edges within the edge detection region that does not represent any object edge; identifying, as a 2D object edge, a candidate edge that is not part of the identified cluster; and performing segmentation based on the 2D object edge.