Keypoint matching improves using lines for global context
CLAD: Keypoint Matching Using Global Line Correspondences
Computer Vision and Pattern RecognitionRobotics
Summary
Finding matching points between images can be tricky when many small areas look the same, like in repetitive patterns. Traditional methods look only at local details, so they can get confused. The authors created a method called CLAD that also considers the overall lines running through the scene, helping to tell points apart by their positions relative to these lines. This makes matching more reliable, especially in hard cases, and the method is efficient to use.
What this means in practice
- •For robotics teams: Improve robot navigation and mapping accuracy by using keypoint matches that incorporate global line structures in scenes with repetitive textures.
- •For augmented reality developers: Enhance environmental understanding for AR applications by combining local features with line context to improve keypoint matching reliability.
Authors
Masaki Satoh
Abstract
Traditional keypoint descriptors, such as SIFT (Scale-Invariant Feature Transform) and ORB (Oriented FAST and Rotated BRIEF), are computed from local image patches. They may be unreliable in scenes with repetitive patterns or weak textures, where patches at different locations can look similar. We propose the Contextual Line-Assisted Descriptor (CLAD), a keypoint matching method that addresses this problem by using global geometric context from line correspondences. CLAD represents each keypoint with a vector whose elements indicate its proximity to matched lines. It can therefore use the keypoint's spatial relationships to the lines in the scene. We use HOME (Hough-space One-dimensional Matching of Extrema) to detect both clear and weak linear structures and to establish enough line-to-keypoint associations to represent this context. Evaluations on the TUM RGB-D SLAM dataset and outdoor video sequences show that CLAD provides geometrically consistent matches that complement those obtained with traditional local descriptors. In repetitive scenes, where local descriptors may not distinguish similar patches, CLAD uses global structure to improve matching reliability. The method is computationally lightweight and practical to implement.