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arXiv · 2510.14354

Leveraging Cycle-Consistent Anchor Points for Self-Supervised RGB-D Registration

Siddharth Tourani·Jayaram Reddy·Sarvesh Thakur·K Madhava Krishna·Muhammad Haris Khan·N Dinesh Reddy
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Abstract

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.

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Siddharth Tourani, Jayaram Reddy, Sarvesh Thakur, K Madhava Krishna, Muhammad Haris Khan, N Dinesh Reddy. 2025-10-16. Leveraging Cycle-Consistent Anchor Points for Self-Supervised RGB-D Registration. https://doi.org/10.1109/icra57147.2024.10610738

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