Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds

Abstract: The point cloud representation of an object can have a large geometric variation in view of inconsistent data acquisition procedure, which thus leads to domain discrepancy due to diverse and uncontrollable shape representation cross datasets. To improve discrimination on unseen distribution of point-based geometries in a practical and feasible perspective, this paper proposes a new method of geometry-aware self-training (GAST) for unsupervised domain adaptation of object point cloud classification. Specifically, this paper aims to learn a domain-shared representation of semantic categories, via two novel self-supervised geometric learning tasks as feature regularization. On one hand, the representation learning is empowered by a linear mixup of point cloud samples with their self-generated rotation labels, to capture a global topological configuration of local geometries. On the other hand, a diverse point distribution across datasets can be normalized with a novel curvature-aware distortion localization. Experiments on the PointDA-10 dataset show that our GAST method can significantly outperform the state-of-the-art methods.

• The paper presents Geometry-Aware Self-Training (GAST), a novel method for unsupervised domain adaptation (UDA) on object point clouds that tackles domain discrepancy from geometric variations.
• GAST combines self-paced semantic feature adaptation with self-supervised geometric encoding using rotation angle prediction and distortion localization tasks to enhance domain invariance.
• Experimental results on the PointDA-10 dataset show GAST significantly outperforms existing methods for UDA on point clouds, improving cross-domain generalization for 3D object recognition applications.

Geometry-Aware Self-Training for Unsupervised Domain Adaptation on Object Point Clouds

This paper presents a novel approach termed Geometry-Aware Self-Training (GAST) tailored for unsupervised domain adaptation (UDA) in the context of object point cloud classification. The inherent challenge tackled by the authors is the domain discrepancy arising from geometric variations in point cloud representations due to diverse and uncontrollable shape acquisition processes. Existing approaches like explicit feature alignment and self-supervised feature encoding encounter limitations that the proposed GAST method aims to overcome by integrating self-training with self-supervised learning.

Methodology Overview

The GAST framework is built upon two principal components:

1. Self-Paced Semantic Feature Adaptation: The paper advocates leveraging a self-training strategy combined with self-paced learning to refine semantic representations across domains. Using labeled data from a source domain and automatically generated pseudo labels for the unlabeled target domain, the method iteratively refines its classification model in an easy-to-hard learning manner. This iterative self-training allows the model to adapt semantic representations to better align with the target domain's characteristics.
2. Self-Supervised Geometric Feature Encoding: The novelty of GAST lies in coupling semantic adaptation with the self-supervised learning of geometric features through two auxiliary tasks—rotation angle prediction and distortion location prediction. The rotation angle prediction task employs rotation of shape primitives and focuses on capturing global topological configurations. The distortion localization task utilizes curvature-based distortion labels to encode local geometric robustness, substantially enhancing domain invariance.

Results and Analysis

The experimental validation on the PointDA-10 dataset corroborates the effectiveness of the proposed GAST method. Notably, GAST significantly outpaces existing SOTA methods in UDA for point clouds, with improvements evident in both synthetic-to-real adaptation tasks. The model showcases a promising generalization capability, particularly for tasks such as M $\rightarrow$ S* and S $\rightarrow$ S*, which are realistically relevant but challenging due to real-world sensor noise and partial occlusions.

The paper provides a comprehensive analysis of component contributions, accentuating the synergistic effects when the self-paced semantic adaptation is jointly utilized with geometry-aware regularization tasks. This amalgamation substantiates improved semantic and geometric discrimination and manifests in robust cross-domain generalization.

Implications and Future Prospects

The implications of this research are multifaceted. Practically, GAST can enhance 3D object recognition systems employing point clouds from various acquisition sources, thereby broadening machine learning applications in autonomous driving, robotics, and augmented reality among others. Theoretically, the integration of self-supervised geometric encoding underlines the growing significance of auxiliary tasks in facilitating cross-domain representation learning.

Future developments may explore extending the core principles of GAST to more complex hierarchical structures and expanding its applicability to other domains rich with geometric data. Furthermore, scrutinizing the integration with neural architecture search techniques may yield optimized models that are both computationally efficient and adaptive.

In conclusion, this paper contributes an innovative methodology that bridges domain discrepancies in point cloud classification, setting a valuable framework for ongoing research in domain adaptation, particularly as it pertains to intricate 3D data representations.