Mitigating Performance Discrepancy in Cross-Domain 3D Class-Incremental Learning

2026-09-04

AI Summary

本文针对3D类增量学习中的性能差异问题，提出了一种无需样本的PolyMem方法，通过隐式建模特征分布的高阶统计量来提高跨域鲁棒性。

Abstract

3D perception plays a crucial role in real-world applications such as autonomous driving, robotics, and AR/VR. In practical scenarios, 3D perception models need to continually adapt to newly emerging 3D object categories, making class-incremental learning (CIL) particularly important. However, unlike 2D images, 3D point clouds are inherently heterogeneous: objects from the same class may not only come from the clean CAD domain, but also from RGB-D camera scans of varying quality, video reconstructions, or even corrupted observations. We discover that such heterogeneity introduces a new challenge beyond catastrophic forgetting: the degree of performance degradation can vary substantially across domains, a phenomenon we term performance discrepancy. To investigate this problem, we establish the Domain3D-CIL training and evaluation protocol, which contains point cloud categories from heterogeneous domains. We further adapt a wide range of mainstream CIL methods to the 3D modality. The results demonstrate that this performance discrepancy consistently appears across these baselines. To mitigate this issue, we introduce PolyMem, an exemplar-free approach that implicitly models rich high-order statistics of the feature distribution to enhance cross-domain robustness. Experiments demonstrate that our method effectively alleviates the performance discrepancy while improving the model's performance across domains. Code will be made publicly available upon acceptance.

Problem

• Cross-Domain
• 3D Class-Incremental Learning
• Performance Discrepancy
• Heterogeneous Data

Innovation

• Cross-Domain
• Class-Incremental Learning
• Performance Discrepancy
• PolyMem