Unsupervised Domain Adaptation on Point Clouds via High-Order Geometric Structure Modeling

Computer Science Information Science Artificial Intelligence
point clouds unsupervised domain adaptation high-order geometric structures contrastive learning self-supervised learning 3D vision feature alignment

High-Order Geometric Structure Modeling-Based Unsupervised Domain Adaptation for Point Clouds

Research Background and Motivation

Point cloud data is a key data form for describing three-dimensional spaces, widely used in real-world applications such as autonomous driving and remote sensing. Point clouds can capture precise geometric information, but when applied across devices or scenarios, their geometric characteristics may undergo significant changes due to sensor noise, sampling methods, and environmental impacts. These significant geometric changes, known as domain gaps, lead to neural networks trained in one domain struggling to maintain performance in another domain. This issue limits the application of deep learning-based point cloud methods in real-world use cases.

Currently, Unsupervised Domain Adaptation (UDA) provides an effective approach to solving this problem. Its core objective is to transfer the knowledge of a source domain (labeled data) to a target domain (unlabeled data) by learning shared cross-domain feature representations to reduce domain gaps. However, existing methods mainly focus on low-order geometric shape features of point clouds while neglecting the potential of high-order geometric structure features (e.g., normals and curvatures). To address this, the paper proposes a novel UDA framework called HO-GSM, which models high-order geometric structures for the first time. The goal is to enhance the model’s ability to capture geometric features comprehensively and improve domain alignment quality.

Paper Source and Author Information

The paper titled “Unsupervised Domain Adaptation on Point Clouds via High-Order Geometric Structure Modeling” was authored by Jiang-Xing Cheng, Huibin Lin, Chun-Yang Zhang, and C.L. Philip Chen (an IEEE Fellow). It was published in IEEE Transactions on Artificial Intelligence (Volume 5, No.12, December 2024). The authors hail from the College of Computer and Data Science, Fuzhou University, and the School of Computer Science and Engineering, South China University of Technology. The paper, published online on October 18, 2024, was supported by the National Natural Science Foundation of China (Grant 62476059).

Research Methodology and Workflow

To address cross-domain adaptation for point clouds, the HO-GSM framework integrates multiple self-supervised tasks and contrastive learning methods, as described below:

1. High-Order Geometric Structure Modeling (HO-GFL)

The innovative core of the model lies in introducing a self-supervised task for high-order geometric structure learning: - Feature Extraction: Using Principal Component Analysis (PCA), local neighborhoods of point clouds are fitted to planes, allowing the computation of high-order features such as normals and curvatures. Normals reflect local connectivity on the point cloud surface, while curvatures measure surface bend and shape variation. This process does not require additional class labels.
- Invariant Encoding: A rotation augmentation operation extracts high-order geometric structure features from rotated point clouds. A mean squared error-based loss function ensures the invariance of these high-order features under augmentation.

2. Low-Order Geometric Shape Feature Learning (LO-GFL)

This module encodes low-order global/local geometric features through two self-supervised tasks: - Global Rotation Angle Prediction: Point clouds are randomly rotated, followed by predicting the rotation angle to learn global shape features. - Local Region Deformation Reconstruction: Voxel-based sampling and reconstruction simulate real-world scanning conditions (e.g., missing parts), enhancing model robustness.

3. Semantic Feature Learning (SFL)

Since no target domain labels are available, the model uses pseudo-labels and a self-paced self-training method to explore the data distribution in the target domain. Pseudo-label generation gradually increases a confidence threshold, ensuring rich semantic feature learning for both source and target domains.

4. Contrastive Learning Module (CONL)

While multi-domain alignment reduces domain gaps, high-quality classification requires ensuring the distinctiveness of aligned features at the class level. HO-GSM employs supervised contrastive learning (using pseudo-labels) to expand positive/negative sample pairs, enhancing the discriminative power of target domain features.

The final training objective combines the contributions of all the above tasks to ensure comprehensive optimization within the network.

Experimental Results

The proposed HO-GSM framework was evaluated on two benchmark datasets to validate its effectiveness:

1. Datasets

  • PointDA-10: Includes three domains (ShapeNet, ModelNet, ScanNet) with 10 shared classes (e.g., “table,” “chair”). It covers diverse scenarios like Synthetic-to-Real and Real-to-Synthetic adaptations.
  • GraspNetPC-10: Features three domains (Kin, RealSense, Synthetic) simulating noise, geometric distortion, and incompleteness in real-world settings.

2. Baseline Methods

Comparison was made against several notable UDA methods, including PointDAN, GAST, and ImplicitPCDA.

3. Performance Highlights

  • Overall Superiority: HO-GSM achieved average classification accuracy of 77.1% on PointDA-10 and 89.2% on GraspNetPC-10, outperforming state-of-the-art methods by 2.0% and 4.8%, respectively.
  • Class-Level Improvements: T-SNE visualization showed that target domain features extracted by HO-GSM were more compact and less confused, especially for closely related classes like “chair” and “cabinet.”
  • Module Contributions: Both high-order geometric structure learning (HO-GFL) and contrastive learning (CONL) were crucial in improving model performance.

Significance and Contributions

  1. Innovations:

    • First to model high-order geometric structure features in UDA for point clouds.
    • A novel way of encoding augmentation invariance for enhanced robustness.

  2. Scientific Value:

    • Advances point cloud neural networks’ cross-domain capabilities, particularly in handling complex real-world scenarios.
    • Broad applications in areas like autonomous driving and 3D vision.

  3. Challenges and Future Directions:

    • The inclusion of multiple self-supervised tasks makes the framework somewhat complex. Future work will explore simplifying the architecture and extending it to domain generalization, aligning more closely with real-world scenarios.

Summary

HO-GSM establishes a new pathway for domain adaptation in point clouds by leveraging high-order geometric structure modeling. It not only overcomes limitations of existing methods but also opens new research possibilities for geometric feature analysis.