3D Mesh Edge Refinement via Texture Gradient Integration
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Solution Overview
Problem
Existing methods for generating 3D mesh models from captured images and depth data often result in inaccuracies, particularly when resolution is reduced for real-time applications, leading to measurement inaccuracies.
Innovation Solution
The approach involves detecting adjacent planes in a mesh model, defining a common edge based on vertices, and refining this edge using texture data from captured images. This includes converting RGB images to grayscale, detecting peak gradients, and combining mesh-based and grayscale-based lines to define the edge more accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resolution is reduced for real-time applications, then processing speed is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the edge refinement process into two distinct stages: (1) mesh-based geometric edge detection that works at lower resolutions, and (2) grayscale-based texture gradient detection that operates on corresponding 2D image regions. This segmentation allows each method to operate optimally at different resolution levels, resolving the contradiction between processing speed and measurement precision.
Solution Approach 2:
The patent transitions from 3D mesh space to 2D image space by mapping mesh vertices to corresponding grayscale image regions. This dimensional change enables the system to leverage texture information from the original high-resolution images without requiring the 3D mesh itself to be at full resolution, thus maintaining measurement precision while preserving processing speed benefits of reduced resolution.
2Device complexity
If only geometric data is used, then processing complexity is reduced, but edge detection accuracy deteriorates
Solution Approach 1:
The patent merges two previously separate data sources: (1) geometric information from the 3D mesh model obtained through depth sensing, and (2) texture information from the corresponding grayscale regions in captured images. By combining these complementary data sources, the system achieves superior edge detection accuracy that leverages both geometric structure and surface texture characteristics.
Solution Approach 2:
The patent introduces an intermediary mapping relationship between 3D mesh vertices and 2D grayscale image regions. This intermediary connection enables the integration of geometric and texture data without requiring direct processing of both full-resolution datasets simultaneously, thus managing processing complexity while achieving high edge detection accuracy.
Data Source
AI summary
A mesh model of a 3D space is provided with improved accuracy by refining the locations of edges of objects in the space. The mesh model includes vertices which define surfaces of triangles. Triangles are identified which have two vertices in one plane and another, outlier vertex in another, adjacent plane. A line is fitted to the outlier vertices to define an edge of an object, and the outlier vertices are moved to the line, referred to as a mesh-based line. Texture data from images of the space can be used to further refine the edge. In one approach, gradients in grayscale pixels which correspond the vertices of the mesh-based line are used to define a grayscale-based line. The two line definitions can be combined or otherwise used to provide a final definition of the edge. The object can be measured based on the length and position of the edge.