Rotation-Adaptive Point Cloud Domain Generalization Via Intricate Orientation Learning

2025-02-04

IEEE Transactions on Pattern Analysis and Machine Intelligence

AI Summary

Existing 3D point cloud analysis methods exhibit fragile cross-domain generalization under arbitrary rotations. Method: This paper introduces “orientation-aware 3D domain generalization,” a novel paradigm featuring (i) a first-of-its-kind complex-orientation optimization mechanism and an orientation-aware contrastive learning framework; (ii) jointly designed orientation-consistency loss and margin-separation loss to jointly enhance class discriminability and rotation invariance; and (iii) iterative hard-example mining to construct a complex-orientation sample set, integrated with rotation-invariant feature representation and domain-generalization regularization. Contribution/Results: The method achieves state-of-the-art performance on multiple 3D cross-domain benchmarks, reducing average generalization error by 12.7%—significantly outperforming existing rotation-augmentation and rotation-invariance modeling approaches.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Addresses rotation vulnerability in 3D point cloud analysis
• Enhances cross-domain robustness via intricate orientation learning
• Proposes rotation-adaptive framework for domain generalization

Innovation

• Rotation-adaptive domain generalization
• Intricate orientation learning
• Orientation-aware contrastive learning