SGAD: Semantic and Geometric-aware Descriptor for Local Feature Matching
Local feature matching remains a fundamental challenge in computer vision. Recent Area to Point Matching (A2PM) methods have improved matching accuracy. However, existing research based on this framework relies on inefficient pixel-level comparisons and complex graph matching that limit scalability. In this work, we introduce the Semantic and Geometric-aware Descriptor Network (SGAD), which fundamentally rethinks area-based matching by generating highly discriminative area descriptors that enable direct matching without complex graph optimization. This approach significantly improves both accuracy and efficiency of area matching. We further improve the performance of area matching through a novel supervision strategy that decomposes the area matching task into classification and ranking subtasks. Finally, we introduce the Hierarchical Containment Redundancy Filter (HCRF) to eliminate overlapping areas by analyzing containment graphs. SGAD demonstrates remarkable performance gains, reducing runtime by 60x (0.82s vs. 60.23s) compared to MESA. Extensive evaluations show consistent improvements across multiple point matchers: SGAD+LoFTR reduces runtime compared to DKM, while achieving higher accuracy (0.82s vs. 1.51s, 65.98 vs. 61.11) in outdoor pose estimation, and SGAD+ROMA delivers +7.39% AUC@5° in indoor pose estimation, establishing a new state-of-the-art.
Code (0)
등록된 구현이 없습니다.
Tasks
Pose EstimationGraph MatchingSimilar Papers 제목 키워드 기반
GESS: Multi-cue Guided Local Feature Learning via Geometric and Semantic Synergy
Robust local feature detection and description are foundational tasks in computer vision. Existing methods primarily rely on single appearance cues for modeling, leading to unstable keypoints and insufficient descriptor …
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
Successful point cloud registration relies on accurate correspondences established upon powerful descriptors. However, existing neural descriptors either leverage a rotation-variant backbone whose performance declines un…
Point Cloud RegistrationPinNet: Keypoint-Aware Learned Local Descriptors with Geometric Embedding for Loop Closure in LiDAR SLAM
Loop closure is essential to reduce drift and build globally consistent maps in large-scale environments. However, reliable loop closure with only geometric information from, e.g., a LiDAR sensor, remains challenging due…
Pose EstimationPoint CloudsSemantic-Enhanced Cross-Modal Place Recognition for Robust Robot Localization
Ensuring accurate localization of robots in environments without GPS capability is a challenging task. Visual Place Recognition (VPR) techniques can potentially achieve this goal, but existing RGB-based methods are sensi…
Visual Place RecognitionContrastive LearningGeometric MatchingSemantic Visual Localization
Robust visual localization under a wide range of viewing conditions is a fundamental problem in computer vision. Handling the difficult cases of this problem is not only very challenging but also of high practical releva…
Visual Localization