3D Point Selection Edge Snapping via Disparity Detection
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Solution Overview
Problem
Current information handling systems face challenges in accurately determining the dimensions of objects within three-dimensional images, particularly when edges are not clearly defined or visible, and in efficiently selecting points of interest in 3D images for measurement purposes.
Innovation Solution
The system employs a processor that uses disparity detection between pixels to identify edges and redefine selection points based on 3D and 2D data, allowing for automatic object selection and dimension calculation without user interaction, utilizing (X, Y, Z) coordinates to calculate distances and display wireframes of objects, and incorporates a method for redefining depth-based edge snapping for precise point selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional point selection methods are used in 3D images, then user interaction is required to select points, but this reduces measurement efficiency and productivity
Solution Approach 1:
The system automatically detects edges and selects measurement points without user intervention. The processor analyzes the 3D image data, identifies edges based on disparity thresholds, and autonomously determines optimal measurement points, allowing the system to serve itself rather than requiring continuous user input.
Solution Approach 2:
The system performs preliminary edge detection and point selection before the actual measurement process. By pre-identifying edges and selecting candidate points in advance, the system prepares the measurement framework beforehand, eliminating the need for users to manually select points during measurement operations.
2Measurement precision
If manual point selection is used for edge detection, then selection accuracy may be achieved, but this increases the time required for measurement operations
Solution Approach 1:
The system replaces manual mechanical point selection with automated computational processing. The processor uses algorithms to detect edges by analyzing pixel disparity in 3D images, automatically identifying edges without requiring manual user input, thereby maintaining precision while dramatically reducing measurement time.
Solution Approach 2:
The system introduces an intermediary automated edge detection process between the raw 3D image data and the final measurement points. This intermediary layer analyzes pixel disparities and automatically identifies edges, serving as a bridge that eliminates the need for direct manual point selection while preserving measurement accuracy.
3Measurement precision
If depth-based edge snapping is used, then edge detection accuracy is improved, but the system complexity increases due to disparity calculation requirements
Solution Approach 1:
The system changes the parameter used for edge detection from manual coordinate input to automated pixel disparity analysis. By utilizing the Z-coordinate disparity between left and right images in a stereoscopic 3D system, the method automatically calculates depth information to identify edges, improving accuracy while managing complexity through parameter transformation rather than additional hardware.
Data Source
AI summary
An information handling system and method includes a display screen for displaying a three dimensional image captured via a three dimensional camera and the processor to detect a selection of a first pixel within the three dimensional image that is proximate to an edge of a first object in the three dimensional image and redefine the selected first pixel to snap to a second pixel within the three dimensional image on the edge of the first object, wherein the second pixel has a large disparity within the three dimensional image and the processor to detect a selection of a third pixel within the three dimensional image that is proximate to an edge of a second object in the three dimensional image and redefine the selected third pixel to snap to a fourth pixel within the three dimensional image on the edge of the second object, wherein the fourth pixel has a large disparity within the three dimensional image.