Cross-Modal Prior-Guided Training with Visual Foundation Models for Unsupervised LiDAR Point Cloud Registration

Abstract

Visual Foundation Models (VFMs) have demonstrated strong capabilities in visual-geometric tasks such as image matching and 3D reconstruction. Recent work has revealed the potential of VFMs in label-free indoor RGB-D registration, where the visual modality is intrinsic to the task. A natural question follows: can this success extend to more challenging outdoor scenarios characterized by sparse, irregular LiDAR data with only partial visual coverage? We answer this with PROMOTER, a novel teacher–student framework for unsupervised LiDAR registration that bridges visual priors and sparse LiDAR geometry. To leverage pre-trained geometric priors for 3D registration, we must disentangle them from confounding signals introduced by modality and task gaps under partial visual coverage. To this end, we introduce a lightweight Adaptive Prior Integrator that extracts and integrates these priors into an enhanced teacher, together with a Perceptual-Geometric Labeler that produces high-quality pseudo-labels. For student training, we propose Matchability-Anchored Training to improve robustness under noisy supervision. Experiments on KITTI and nuScenes demonstrate state-of-the-art performance and improved scalability across registration models and VFMs, while incurring no additional parameters at inference. Code will be released.

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