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

2025-10-16

AI Summary

To address geometric reasoning from unlabeled RGB-D data, this paper proposes a self-supervised point cloud registration framework. The method introduces (1) a cycle-consistent keypoint selection mechanism that enforces cross-view spatial constraints via geometrically salient anchor points, and (2) a pose estimation module integrating GRU-based temporal modeling with transformation synchronization, jointly leveraging historical observations and multi-view geometric consistency. Without requiring any manual annotations, the framework achieves state-of-the-art performance among self-supervised approaches on ScanNet and 3DMatch—matching or even surpassing certain fully supervised baselines. It further demonstrates strong generalizability: the learned representations can be readily integrated into downstream SLAM or reconstruction systems, significantly improving their robustness and accuracy.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Utilizing unlabeled RGB-D data for geometric scene reasoning
• Improving correspondence accuracy through cycle-consistent keypoints
• Developing novel pose estimation combining GRU and transformation synchronization

Innovation

• Uses cycle-consistent keypoints for spatial coherence
• Introduces GRU-based pose block with transformation synchronization
• Blends historical and multi-view data for registration