RAGE-I2P: Reliability-Aware Geometric Embeddings for Image-to-Point Cloud Registration in Changing Environments

Existing image-to-point cloud registration methods have achieved strong performance in static scenes. However, real-world environments often contain moved, removed, or newly added objects. Such changes can preserve feature similarity while breaking physical correspondence, producing unreliable matches at both coarse and fine levels. We propose RAGE-I2P, a geometry-guided framework for reliable registration in changing environments. We learn coarse- and fine-level geometric embeddings from image and point-cloud normals. At the coarse level, a covariance-aware discrepancy between geometric embeddings provides compatibility weights for cross-modal attention. At the fine level, normal-derived embeddings enrich feature extraction with local surface information, while an additional embedding loss encourages correspondence-discriminative representations. We also propose ASIMLab, a seven-scene dataset captured under real scene changes, and construct Changing RGBDScenesV2 through controlled image edits. Experiments on both benchmarks show that RAGE-I2P achieves the best mean correspondence and registration performance. The source code and the datasets used in the paper will be released soon.