Leveraging Cycle-Consistent Anchor Points for Self-Supervised RGB-D Registration
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914096257957888 |
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| author | Tourani, Siddharth Reddy, Jayaram Thakur, Sarvesh Krishna, K Madhava Khan, Muhammad Haris Reddy, N Dinesh |
| author_facet | Tourani, Siddharth Reddy, Jayaram Thakur, Sarvesh Krishna, K Madhava Khan, Muhammad Haris Reddy, N Dinesh |
| contents | With the rise in consumer depth cameras, a wealth of unlabeled RGB-D data has become available. This prompts the question of how to utilize this data for geometric reasoning of scenes. While many RGB-D registration meth- ods rely on geometric and feature-based similarity, we take a different approach. We use cycle-consistent keypoints as salient points to enforce spatial coherence constraints during matching, improving correspondence accuracy. Additionally, we introduce a novel pose block that combines a GRU recurrent unit with transformation synchronization, blending historical and multi-view data. Our approach surpasses previous self- supervised registration methods on ScanNet and 3DMatch, even outperforming some older supervised methods. We also integrate our components into existing methods, showing their effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14354 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging Cycle-Consistent Anchor Points for Self-Supervised RGB-D Registration Tourani, Siddharth Reddy, Jayaram Thakur, Sarvesh Krishna, K Madhava Khan, Muhammad Haris Reddy, N Dinesh Computer Vision and Pattern Recognition Robotics With the rise in consumer depth cameras, a wealth of unlabeled RGB-D data has become available. This prompts the question of how to utilize this data for geometric reasoning of scenes. While many RGB-D registration meth- ods rely on geometric and feature-based similarity, we take a different approach. We use cycle-consistent keypoints as salient points to enforce spatial coherence constraints during matching, improving correspondence accuracy. Additionally, we introduce a novel pose block that combines a GRU recurrent unit with transformation synchronization, blending historical and multi-view data. Our approach surpasses previous self- supervised registration methods on ScanNet and 3DMatch, even outperforming some older supervised methods. We also integrate our components into existing methods, showing their effectiveness. |
| title | Leveraging Cycle-Consistent Anchor Points for Self-Supervised RGB-D Registration |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2510.14354 |