Dynamic Regional Mesh Subdivision for 3D Measurement Accuracy
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Solution Overview
Problem
Current 3D mesh models generated from captured images and depth data struggle to accurately represent objects attached to walls or coplanar with the wall, leading to inaccuracies in measurements due to limited storage capacity and complexity, especially for features like wall hangings and doors.
Innovation Solution
The technique involves subdividing triangle faces based on user-selected points in a 3D space, adding new vertices where a straight or curved line intersects edges of existing triangles, and automatically identifying points of interest to enhance measurement accuracy and model precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a uniform high-resolution mesh is used to improve measurement accuracy, then measurement precision improves, but device complexity and data storage requirements increase
Solution Approach 1:
The patent divides the 3D space into multiple triangular regions based on detected edges and features. Each triangle is processed independently to determine if it contains objects of interest, allowing selective refinement only in necessary areas rather than uniformly across the entire mesh, thus reducing overall complexity while maintaining measurement accuracy where needed.
Solution Approach 2:
The patent applies different levels of mesh refinement to different regions of the 3D space. Regions containing objects of interest (detected through edge detection and feature analysis) receive higher resolution treatment, while empty or less important regions maintain lower resolution, optimizing the balance between measurement accuracy and data complexity.
2Measurement precision
If more vertices are added to represent objects attached to walls, then measurement accuracy improves, but data storage requirements increase
Solution Approach 1:
The patent segments the mesh into triangular regions and identifies those containing objects attached to walls or coplanar surfaces. By processing each triangle independently and only adding vertices where objects are detected, the system avoids unnecessary vertex addition in empty spaces, reducing overall data storage requirements while maintaining accuracy for objects of interest.
Solution Approach 2:
The patent applies vertex addition selectively only where needed to represent objects of interest, rather than uniformly across the entire mesh. This partial action approach adds the minimum necessary vertices to achieve accurate object representation, avoiding excessive data storage consumption.
3Productivity
If dynamic regional mesh subdivision is used to reduce computational complexity, then processing efficiency improves, but measurement precision may deteriorate
Solution Approach 1:
The patent segments the mesh processing into independent triangular regions that can be processed in parallel. Each triangle is evaluated individually for object presence using edge detection and feature analysis, enabling efficient parallel processing while maintaining measurement accuracy through systematic evaluation of each region.
Solution Approach 2:
The patent performs preliminary edge detection and feature analysis on each triangular region before deciding whether to add vertices. This preliminary action identifies regions requiring refinement, allowing the system to focus computational resources only where needed, thus maintaining measurement precision while improving overall processing efficiency.
Data Source
AI summary
A mesh model of a 3D space is provided with improved accuracy based on user inputs. In one aspect, a triangle face of the mesh is divided into three smaller triangle faces base on a user-selected point in a 3D space. A user can select the point on a display screen, for example, where a corresponding vertex in the mesh is a point in the mesh which is intersected by a ray cast from the selected point. This process can be repeated to provide new vertices in the mesh model which more accurately represent an object in the 3D space and therefore allow a more accurate measurement of the size or area of the object. For example, the user might select four points to identify a rectangular object.