GEAR: Versatile Transductive Diffusion for Training-Free 3D Object Retrieval

Deep 3D embeddings derived from pretrained models have demonstrated promising retrieval performance. However, achieving optimal results typically requires downstream adaptation with labeled 3D training data, which incurs significant annotation costs and limits their applicability in evolving real-world scenarios. In this paper, we introduce GEAR, a training-free test-time adaptation framework that efficiently performs transductive diffusion to improve the embedding discriminativeness without requiring labeled 3D data for adaptation. Specifically, GEAR only exploits the proximity information among unlabeled gallery objects and diffuses in a transductive manner. In this way, we enforce a cohesive embedding space for gallery samples suitable for retrieval. Unlike conventional transductive approaches that require a batch of queries for joint adaptation, GEAR enables single-query adaptation during inference by leveraging the refined gallery representations, making it applicable to standard online retrieval scenarios. Furthermore, we introduce an iterative anchored diffusion strategy to improve retrieval ranking while preserving the original query evidence. Extensive experiments on 3D object retrieval benchmarks demonstrate that GEAR effectively boosts the performance of various pretrained representations. Additional evaluations on sketch-based and scene-level retrieval further verify its flexibility and generalizability.