Multi-Scale Part-Based Feature Representation for 3D Domain Generalization and Adaptation.

Deep networks for 3D point clouds have achieved remarkable success in classification task but remain vulnerable to geometric variations resulting from inconsistent data acquisition procedures. This leads to significant performance degradation when models trained on a source domain are tested on out-of-distribution target domains, highlighting the challenges of 3D domain generalization and adaptation. In this paper, we introduce a novel Multi-Scale Part-based feature Representation, dubbed MSPR, as a generalizable representation for point cloud domain generalization and adaptation. Rather than relying on global shape feature, we align the part-level features of shapes at different scales to a set of learnable part-template features that encode local geometric structures shared between the source and the target domains. Specifically, shapes from different domains are organized into part-level features at various scales and then aligned to the part-template features. To balance the generalization and discrimination abilities of parts at different scales, we further design a cross-scale feature fusion module to exchange information between aligned part-based features at different scales. The fused part-based representations are finally aggregated by a part-based feature aggregation module. To improve the robustness of the aligned part-based representations and global shape representation to geometry variations, we further propose a Contrastive Learning framework on Shape Representation (CLSR). Experiments are conducted on 3D domain generalization and adaptation benchmarks for point cloud classification. Extensive experiments on 3D domain generalization and adaptation benchmarks demonstrate that proposed approach outperforms previous state-of-the-art methods in both tasks. Ablation studies confirm the effectiveness of the components in our model.