RGB-D Multi-View Matching With Variable Templates for 3D Reconstruction
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Solution Overview
Problem
Existing RGB-D camera multi-view matching methods face challenges with accumulation of matching errors and mismatches due to excessive rotation or scaling between non-adjacent images, leading to difficulties in three-dimensional reconstruction, especially in scenes requiring large angles of view.
Innovation Solution
A digital image calculation method using a variable template for RGB-D camera multi-view matching, involving data acquisition, preprocessing, feature point matching, re-registering a variable template, calculating transformation relationships, and performing point cloud fusion to adjust the template size based on registration results, ensuring accurate registration and reducing cumulative errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If intermediate images are inserted to incrementally accumulate matching results, then matching coverage is improved, but computational costs increase and cumulative errors are caused
Solution Approach 1:
The patent pre-calculates and stores transformation relationships between non-adjacent images before actual matching. By preparing these transformation matrices in advance, the system avoids the need to insert multiple intermediate images during runtime, thus reducing computational costs while maintaining comprehensive matching coverage across large angle changes.
Solution Approach 2:
The patent introduces transformation relationship matrices as intermediaries between non-adjacent images. Instead of directly matching images with large angle differences or inserting multiple intermediate images, the system uses pre-computed transformation matrices to bridge the gap, eliminating cumulative errors while reducing computational burden.
2Reliability
If feature point matching methods like SIFT are used, then matching robustness is improved, but dependency on feature point quantity increases
Solution Approach 1:
The patent changes the approach from relying on feature point quantity to using transformation relationship matrices that encode geometric constraints. By transforming the matching problem into a parameter-based transformation application, the system maintains robustness without being dependent on having sufficient feature point pairs, thus solving the contradiction between reliability and feature point quantity requirements.
3Measurement precision
If adjacent image matching is performed with limited angle constraints, then matching accuracy is improved, but viewing angle coverage is restricted
Solution Approach 1:
The patent extends the matching capability from adjacent images to non-adjacent images by introducing transformation relationship matrices as an additional dimensional tool. This allows the system to maintain high matching accuracy while covering large viewing angles, as the transformation matrices provide the necessary geometric guidance for matching images that are not adjacent in the sequence.
Data Source
AI summary
Disclosed is a digital image calculation method and system for RGB-D camera multi-view matching based on a variable template, the method includes six steps: acquiring data, preprocessing point cloud data, performing feature point matching, re-registering a variable template, calculating point cloud data transformation relationships among large-view images, and performing point cloud fusion. A size of a non-adjacent image matching template is adjusted based on registration results of adjacent angles of view, and correct registration of feature points of images from non-adjacent angles of view is accordingly achieved, which improves matching accuracy, eliminates cumulative errors in image sets, and provides more accurate initial values for subsequent iterations of point cloud fusion, such that the number of iterations is reduced, and three-dimensional reconstruction of images is implemented.