用少量数据让激光雷达3D检测模型跨域适配，效果显著提升。
Finetuning Pre-trained Model with Limited Data for LiDAR-based 3D Object Detection by Bridging Domain Gaps
- 师生架构中引入双层正则化对齐表征，缓解域偏移问题。
- 仅用约100帧目标数据，即在Waymo和KITTI上实现精度大幅提升。
- 适合传感器配置不同或标注数据稀缺的自动驾驶场景。
基于激光雷达的3D目标检测器在自动驾驶、移动机器人等领域广泛应用。然而，当目标域存在传感器配置差异（如传感器类型、空间分辨率或视场角）或地理位置偏移时，现有检测器性能下降明显。通常需在新场景下收集并标注数据以减少域差距，但成本高昂且耗时。近期研究提出可通过大规模无标签激光雷达帧自监督训练预训练骨干网络，但其在缺乏目标域大量数据时仍难以良好泛化。为此，本文提出一种新方法——域自适应蒸馏微调（DADT），可在仅有约100帧目标数据的情况下，有效微调预训练模型，保持其表示能力并防止过拟合。具体而言，采用正则化项在教师-学生架构中对齐物体级与上下文级表征。在Waymo Open Dataset和KITTI等驾驶基准上的实验表明，该方法能显著提升微调效果。
原文摘要 · Abstract (English)
LiDAR-based 3D object detectors have been largely utilized in various applications, including autonomous vehicles or mobile robots. However, LiDAR-based detectors often fail to adapt well to target domains with different sensor configurations (e.g., types of sensors, spatial resolution, or FOVs) and location shifts. Collecting and annotating datasets in a new setup is commonly required to reduce such gaps, but it is often expensive and time-consuming. Recent studies suggest that pre-trained backbones can be learned in a self-supervised manner with large-scale unlabeled LiDAR frames. However, despite their expressive representations, they remain challenging to generalize well without substantial amounts of data from the target domain. Thus, we propose a novel method, called Domain Adaptive Distill-Tuning (DADT), to adapt a pre-trained model with limited target data (approximately 100 LiDAR frames), retaining its representation power and preventing it from overfitting. Specifically, we use regularizers to align object-level and context-level representations between the pre-trained and finetuned models in a teacher-student architecture. Our experiments with driving benchmarks, i.e., Waymo Open dataset and KITTI, confirm that our method effectively finetunes a pre-trained model, achieving significant gains in accuracy.
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