用高斯斑点显式建模局部结构，提升激光雷达3D检测跨域泛化能力。
GBlobs: Explicit Local Structure via Gaussian Blobs for Improved Cross-Domain LiDAR-based 3D Object Detection
- 用高斯斑点编码点云邻域，增强局部结构感知。
- 在单源跨域任务上提升超21 mAP，且不损失域内性能。
- 无需额外参数，适配主流检测器，适合自动驾驶场景应用。
基于激光雷达的3D目标检测器需要大量数据训练，但在新领域上泛化能力差。领域泛化（DG）旨在训练对领域变化不变的检测器。现有方法仅依赖全局几何特征（点云笛卡尔坐标），导致检测器过度关注物体位置和绝对坐标，影响跨域性能。为此，本文提出通过高斯斑点（GBlobs）显式编码点云邻域结构，以增强局部结构表征。该方法高效且无需额外参数。仅通过集成GBlobs至现有检测器，即在挑战性的单源跨域基准上超越当前最优：Waymo→KITTI提升21 mAP，KITTI→Waymo提升13 mAP，nuScenes→KITTI提升12 mAP，且不牺牲域内性能。此外，在多源DG中也显著优于当前最优，在Waymo、KITTI、ONCE上分别提升17、12、5 mAP。
原文摘要 · Abstract (English)
LiDAR-based 3D detectors need large datasets for training, yet they struggle to generalize to novel domains. Domain Generalization (DG) aims to mitigate this by training detectors that are invariant to such domain shifts. Current DG approaches exclusively rely on global geometric features (point cloud Cartesian coordinates) as input features. Over-reliance on these global geometric features can, however, cause 3D detectors to prioritize object location and absolute position, resulting in poor cross-domain performance. To mitigate this, we propose to exploit explicit local point cloud structure for DG, in particular by encoding point cloud neighborhoods with Gaussian blobs, GBlobs. Our proposed formulation is highly efficient and requires no additional parameters. Without any bells and whistles, simply by integrating GBlobs in existing detectors, we beat the current state-of-the-art in challenging single-source DG benchmarks by over 21 mAP (Waymo->KITTI), 13 mAP (KITTI->Waymo), and 12 mAP (nuScenes->KITTI), without sacrificing in-domain performance. Additionally, GBlobs demonstrate exceptional performance in multi-source DG, surpassing the current state-of-the-art by 17, 12, and 5 mAP on Waymo, KITTI, and ONCE, respectively.
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