Debiased Prototype Evolving for Point Cloud Domain Adaptation via 3D Foundation Models

Abstract

Domain adaptation in point cloud data is essential for improving downstream tasks in autonomous driving, robotics, and 3D modeling. 3D Foundation models, driven by scaling laws, have significantly advanced point cloud applications by embedding rich semantic knowledge of geometric structures. However, a significant gap remains between their broad zero-shot generalization capabilities and the specialized requirements of domain adaptation tasks. Furthermore, pre-training can induce a model bias towards samples that resemble the pre-training dataset. To bridge this gap, we propose an Evolving Alignment strategy to apply large-scale 3D foundation models to domain adaptation in 3D point clouds, named EvoAlign3D. Specifically, we implement a joint domain alignment strategy to align the foundation model’s feature space with a transferable feature space across the source and target domains. Meanwhile, we propose a debiased prototype evolving method, which refines class-level prototypes and optimizes pseudo-label consistency, progressively mitigating model biases and enhancing the transferability of discriminative features for better cross-domain generalization. Our method significantly improves domain adaptation classification performance on the PointDA dataset, which spans both synthetic and real-world data domains. All the code and pre-trained weights will be publicly available.