仅用激光雷达数据实现跨域自适应，提升3D点云分割精度
LiOn-XA: Unsupervised Domain Adaptation via LiDAR-Only Cross-Modal Adversarial Training
- 利用2D投影图与3D体素图双模态学习，增强几何特征表达
- 在3个真实场景迁移任务中达到新最优，性能超越单/多模态方法
- 适合无摄像头的自动驾驶场景，对传感器缺失鲁棒性强
本文提出LiOn-XA，一种基于激光雷达的无监督域自适应方法，通过3D体素点云与2D投影范围图的跨模态对抗训练，解决因环境和传感器差异导致的领域偏移问题。不同于依赖点云与图像多模态的方法，本方案仅使用激光雷达数据，证明两种不同表示形式可相互协作实现域自适应。其中3D体素保留几何结构，2D投影图提供物体朝向与表面信息。通过联合对抗训练2D与3D神经网络的特征与预测，进一步对齐源域与目标域的特征空间。在3个真实场景到真实场景的迁移任务中验证有效，性能优于现有单模态与多模态方法，达到新基准。代码已开源。
原文摘要 · Abstract (English)
In this paper, we propose LiOn-XA, an unsupervised domain adaptation (UDA) approach that combines LiDAR-Only Cross-Modal (X) learning with Adversarial training for 3D LiDAR point cloud semantic segmentation to bridge the domain gap arising from environmental and sensor setup changes. Unlike existing works that exploit multiple data modalities like point clouds and RGB image data, we address UDA in scenarios where RGB images might not be available and show that two distinct LiDAR data representations can learn from each other for UDA. More specifically, we leverage 3D voxelized point clouds to preserve important geometric structure in combination with 2D projection-based range images that provide information such as object orientations or surfaces. To further align the feature space between both domains, we apply adversarial training using both features and predictions of both 2D and 3D neural networks. Our experiments on 3 real-to-real adaptation scenarios demonstrate the effectiveness of our approach, achieving new state-of-the-art performance when compared to previous uni- and multi-model UDA methods. Our source code is publicly available at https://github.com/JensLe97/lion-xa.
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