Camera Pose Estimation Using Multi-Stage Geometric Semantic Attention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image feature matching algorithms struggle to achieve accurate results, especially under conditions of wide baselines and large variations in lighting, due to the influence of illumination, resolution, angle, and sensor parameters.
Innovation Solution
A camera pose estimation method utilizing a multi-stage geometric semantic attention network to construct an optimization network for mismatch removal, improving the accuracy of feature matching and subsequent camera pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional feature matching algorithms are used, then the matching process can be completed, but the matching accuracy deteriorates under wide baselines and large lighting variations
Solution Approach 1:
The patent segments the feature matching process into multiple stages: initial feature point extraction, initial matching, mismatch removal, and refined matching. Each stage processes the data differently to progressively improve accuracy while filtering out mismatches caused by illumination and geometric variations
Solution Approach 2:
The patent introduces intermediate representations including feature descriptors, matching scores, and confidence metrics that mediate between raw image data and final matching results. These intermediaries help filter out harmful effects by providing multiple levels of verification
2Adaptability or versatility
If feature matching is performed under wide baselines and large lighting variations, then more image pairs can be processed, but the matching accuracy deteriorates
Solution Approach 1:
The patent applies local quality by using feature descriptors that capture local image characteristics around each feature point. This allows the matching to be robust to global illumination changes while maintaining sensitivity to local geometric structures
Solution Approach 2:
The patent dynamically adjusts matching thresholds and confidence criteria based on the specific image pair conditions. The mismatch removal stage dynamically filters matches based on geometric consistency checks that adapt to the baseline and lighting conditions
3Measurement precision
If multiple feature matching algorithms are combined to improve accuracy, then the matching precision improves, but the system complexity increases
Solution Approach 1:
The patent merges multiple processing stages (feature extraction, initial matching, mismatch removal, refined matching) into a unified pipeline. This combines the strengths of different approaches while managing complexity through structured integration
Solution Approach 2:
The patent implements feedback mechanisms where mismatch removal results feed back into the matching process, and confidence metrics from matching inform the selection and tuning of processing parameters. This feedback loop improves accuracy while keeping the system structure manageable
Data Source
AI summary
The present invention relates to the field of artificial intelligence, and in particular, to a camera pose estimation method and system, electronic equipment and a readable medium. The camera pose estimation method includes: acquiring an initial matching set between a first image and a second image, where the first image and the second image are images from different angles for a same scene; performing a mismatch removal operation on the initial matching set based on an optimization network to obtain an optimized matching set, where the optimization network is constructed based on a multi-stage geometric semantic attention network; and acquiring a camera pose result based on the optimized matching set. By removing mismatches, feature matching results between the first image and the second image are more accurate, and thus a more accurate result can be obtained when performing camera pose estimation.