Point Cloud Merging Using Image-Based Feature Alignment
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Solution Overview
Problem
Existing methods for merging three-dimensional point cloud models based on an iterative closest point (ICP) algorithm result in low accuracy due to direct coordinate alignment on point clouds, especially when images are taken under different visual conditions.
Innovation Solution
A method involving image-based feature matching and triangulation to establish spatial relationships between point cloud models, using pre-trained models and algorithms like RANSAC-ICP and PnP to calculate merge parameters, enhancing the alignment and merging process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If ICP algorithm is used for merging point cloud models, then the merging process is simple, but the accuracy of the merged model is low
Solution Approach 1:
The patent introduces an intermediary coordinate transformation process that uses image feature matching and triangulation to establish initial correspondence between point clouds from different views. This intermediary step provides more reliable matching information before applying ICP, thereby improving accuracy while maintaining reasonable process simplicity.
Solution Approach 2:
The patent performs preliminary coordinate alignment using image-based feature matching and triangulation before applying the ICP algorithm. This preliminary action establishes a better initial correspondence between point clouds, which significantly improves the final merging accuracy while keeping the overall process manageable.
2Speed
If direct coordinate alignment on point clouds is performed, then the processing speed is fast, but the accuracy of the merged model is low
Solution Approach 1:
The patent performs preliminary coordinate alignment using image-based feature matching and triangulation before applying the ICP algorithm. This preliminary action establishes a better initial correspondence between point clouds, which significantly improves the final merging accuracy while keeping the overall process manageable.
Solution Approach 2:
The patent replaces direct point cloud coordinate alignment with an image-based feature matching and triangulation system. This substitution uses 2D image correspondence to guide 3D point cloud alignment, providing more accurate initial matching information that improves final accuracy.
3Measurement precision
If more image information is incorporated in the merging process, then the accuracy of the merged model is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the merging process into distinct stages: image feature extraction, feature matching, triangulation to obtain initial correspondence, and finally ICP refinement. This segmentation allows each stage to focus on specific tasks, improving accuracy while managing computational complexity through structured processing.
Solution Approach 2:
The patent uses a hybrid approach that combines image-based feature matching (partial action) with ICP algorithm (excessive action). The image-based approach provides sufficient initial correspondence for many applications, while the ICP algorithm can be applied with reduced iterations since the initial alignment is already good, thus balancing accuracy and complexity.
Data Source
AI summary
Disclosed are a point cloud model processing method and apparatus, and a readable storage medium. The method includes: acquiring a target image pair and first and second neighborhood images corresponding to the target image pair from first and second image sets photographed for a target scene with different visual effects; performing a calculation to obtain a merge parameter based on the relationship among the target image pair, the first neighborhood image, the second neighborhood image, a first point cloud model, and a second point cloud model; and merging the first point cloud model and the second point cloud model based on the merge parameter to obtain a target point cloud model for reconstructing a three-dimensional structure of the target scene.