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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.08333 |
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| _version_ | 1866908530055839744 |
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| author | Gottam, Sai Puneeth Reddy Zhang, Haoming Keras, Eivydas |
| author_facet | Gottam, Sai Puneeth Reddy Zhang, Haoming Keras, Eivydas |
| contents | Visual-based localization has made significant progress, yet its performance often drops in large-scale, outdoor, and long-term settings due to factors like lighting changes, dynamic scenes, and low-texture areas. These challenges degrade feature extraction and tracking, which are critical for accurate motion estimation. While learning-based methods such as SuperPoint and SuperGlue show improved feature coverage and robustness, they still face generalization issues with out-of-distribution data. We address this by enhancing deep feature extraction and tracking through self-supervised learning with task specific feedback. Our method promotes stable and informative features, improving generalization and reliability in challenging environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08333 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry Gottam, Sai Puneeth Reddy Zhang, Haoming Keras, Eivydas Robotics Computer Vision and Pattern Recognition Visual-based localization has made significant progress, yet its performance often drops in large-scale, outdoor, and long-term settings due to factors like lighting changes, dynamic scenes, and low-texture areas. These challenges degrade feature extraction and tracking, which are critical for accurate motion estimation. While learning-based methods such as SuperPoint and SuperGlue show improved feature coverage and robustness, they still face generalization issues with out-of-distribution data. We address this by enhancing deep feature extraction and tracking through self-supervised learning with task specific feedback. Our method promotes stable and informative features, improving generalization and reliability in challenging environments. |
| title | Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.08333 |