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Found 4 papers, 1 papers with code
MESA: Matching Everything by Segmenting Anything
However, the pervasive presence of matching redundancy between images gives rise to unnecessary and error-prone computations in these methods, imposing limitations on their accuracy.
Searching from Area to Point: A Hierarchical Framework for Semantic-Geometric Combined Feature Matching
This paper, thus, pays attention to the search space and proposes to set the initial search space for point matching as the matched image areas containing prominent semantic, named semantic area matches.
Learning-Based Framework for Camera Calibration with Distortion Correction and High Precision Feature Detection
Therefore, in this paper, we propose a hybrid camera calibration framework which combines learning-based approaches with traditional methods to handle these bottlenecks.
An End to End Network Architecture for Fundamental Matrix Estimation
In this paper, we present a novel end-to-end network architecture to estimate fundamental matrix directly from stereo images.
