SELC: 低品質画像のための自己教師あり効率的局所対応学習
SELC: Self-Supervised Efficient Local Correspondence Learning for Low Quality Images
手動アノテーション不要のハイブリッド自己教師あり学習で、低品質画像でも高解像度時に2〜10倍効率的な軽量特徴マッチングネットワークを提案。
著者: Yuqing Wang, Yan Wang, Hailiang Tang, Xiaoji Niu
分類: cs.RO
原文アブストラクト
Accurate and stable feature matching is critical for computer vision tasks, particularly in applications such as Simultaneous Localization and Mapping (SLAM). While recent learning-based feature matching methods have demonstrated promising performance in challenging spatiotemporal scenarios, they still face inherent trade-offs between accuracy and computational efficiency in specific settings. In this paper, we propose a lightweight feature matching network designed to establish sparse, stable, and consistent correspondence between multiple frames. The proposed method eliminates the dependency on manual annotations during training and mitigates feature drift through a hybrid self-supervised paradigm. Extensive experiments validate three key advantages: (1) Our method operates without dependency on external prior knowledge and seamlessly incorporates its hybrid training mechanism into original datasets. (2) Benchmarked against state-of-the-art deep learning-based methods, our approach maintains equivalent computational efficiency at low-resolution scales while achieving a 2-10x improvement in computational efficiency for high-resolution inputs. (3) Comparative evaluations demonstrate that the proposed hybrid self-supervised scheme effectively mitigates feature drift in long-term tracking while maintaining consistent representation across image sequences.
