Rotation-Adaptive Point Cloud Domain Generalization via Intricate Orientation Learning

TL;DR

This paper introduces a rotation-adaptive domain generalization framework for 3D point cloud analysis, leveraging intricate orientations and contrastive learning to improve robustness against unpredictable rotations across domains.

Contribution

It proposes an innovative approach that identifies challenging rotations, constructs intricate orientation sets, and employs orientation-aware contrastive learning for enhanced 3D domain generalization.

Findings

Achieves state-of-the-art results on 3D cross-domain benchmarks.

Effectively learns rotation-consistent and discriminative features.

Demonstrates robustness to orientation shifts in 3D point clouds.

Abstract

The vulnerability of 3D point cloud analysis to unpredictable rotations poses an open yet challenging problem: orientation-aware 3D domain generalization. Cross-domain robustness and adaptability of 3D representations are crucial but not easily achieved through rotation augmentation. Motivated by the inherent advantages of intricate orientations in enhancing generalizability, we propose an innovative rotation-adaptive domain generalization framework for 3D point cloud analysis. Our approach aims to alleviate orientational shifts by leveraging intricate samples in an iterative learning process. Specifically, we identify the most challenging rotation for each point cloud and construct an intricate orientation set by optimizing intricate orientations. Subsequently, we employ an orientation-aware contrastive learning framework that incorporates an orientation consistency loss and a margin separation loss, enabling effective learning of categorically discriminative and generalizable features with rotation consistency. Extensive experiments and ablations conducted on 3D cross-domain benchmarks firmly establish the state-of-the-art performance of our proposed approach in the context of orientation-aware 3D domain generalization.