VIPER: Visual Prior Retrieval for Multi-Modal Point Cloud Completion

We introduce VIPER, a simple and effective visual prior retrieval framework for multi-modal point cloud completion. Explicit image-derived 3D priors can provide structural guidance, but inaccuracies in these priors may introduce misleading geometric cues, and generating them incurs substantial computational overhead. VIPER instead treats image representations extracted by a frozen vision foundation model as queryable visual priors, using partial geometry to retrieve evidence relevant to missing or ambiguous structures. The retrieved priors are adaptively incorporated into a persistent shape representation, enabling them to guide both global shape recovery and progressive geometric refinement. On ShapeNet-ViPC, VIPER achieves state-of-the-art performance with 16.4% lower average Chamfer Distance and a 125$\times$ end-to-end inference speedup over PGNet. We further introduce OmniObject3D-Completion, a joint-category benchmark with broad object coverage for developing and evaluating multi-modal point cloud completion methods.