3D-2D Edge Matching for Faster Object Extraction Labeling
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Solution Overview
Problem
Existing methods for data labeling in object segmentation are time-consuming and prone to errors, necessitating efficient and accurate labeling techniques for AI learning data.
Innovation Solution
An object extraction method and system utilizing a pre-processing module to generate an edge depth map, an edge module to extract target edges from 3D data, and an optimization module to optimize these edges to match the depth map, employing multiple edge extraction algorithms and cost function minimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual edge separation is performed by hand for data labeling, then labeling accuracy can be maintained, but the time required for labeling work increases significantly
Solution Approach 1:
The system performs preliminary edge extraction from 3D data to generate a target edge map before the actual labeling task. This pre-extracted edge information serves as a foundation that guides subsequent optimization and matching processes, eliminating the need for time-consuming manual edge separation while maintaining accuracy
Solution Approach 2:
The system employs an optimization module that iteratively adjusts parameters of the target edge by comparing it with the edge depth map generated from 2D images. This feedback mechanism continuously refines the extracted edges until optimal matching is achieved, ensuring high labeling accuracy automatically without manual intervention
2Measurement precision
If multiple edge extraction algorithms with different parameters are applied, then edge detection accuracy improves, but the complexity of the processing system increases
Solution Approach 1:
The system divides the edge extraction process into distinct functional modules: a pre-processing module that generates edge depth maps from 2D images, an edge module that extracts target edges from 3D data using multiple algorithms, and an optimization module that refines the results. This segmentation allows each module to specialize in specific algorithms and parameters without overwhelming the entire system
Solution Approach 2:
The system systematically varies parameters across multiple edge extraction algorithms (such as Canny, Sobel, Laplacian) by setting different parameter values for each algorithm. This approach enables comprehensive edge detection across different conditions while managing complexity through structured parameter exploration rather than ad-hoc adjustments
3Productivity
If automated edge extraction from 3D data is performed, then labeling efficiency increases, but the accuracy may decrease due to algorithmic limitations
Solution Approach 1:
The system merges two different approaches: automated 3D-to-2D edge projection and 2D image-based edge detection. By combining the target edge from 3D data with the edge depth map from 2D images through optimization and matching, the system leverages the efficiency of automated extraction while compensating for algorithmic limitations using visual feedback from actual images
Solution Approach 2:
The system transitions from 3D space to 2D space by projecting 3D object edges onto the 2D image plane. This dimensional transformation allows the system to utilize 3D geometric information for efficient edge extraction while validating and refining results against 2D visual data, combining advantages of both dimensional perspectives
Data Source
AI summary
An object extraction method using an object extraction system that includes a pre-processing module, an edge module, and an optimization module, and the object extraction method includes: generating an edge depth map with respect to a 2D image by pre-processing a 2D image where a target object to be data-labeled may be included, by the pre-processing module; extracting a target edge with respect to the target object from 3D data where the target object may be included, by the edge module; and optimizing the generated target edge to a final edge to match the generated edge depth map, by the optimization module.