In the field of autonomous driving, self-training is widely applied to mitigate distribution shifts in LiDAR-based 3D object detectors. This eliminates the need for expensive, high-quality labels whenever the environment changes (e.g., geographic location, sensor setup, weather condition). State-of-the-art self-training approaches, however, mostly ignore the temporal nature of autonomous driving data. To address this issue, we propose a flow-aware self-training method that enables unsupervised domain adaptation for 3D object detectors on continuous LiDAR point clouds. In order to get reliable pseudo-labels, we leverage scene flow to propagate detections through time. In particular, we introduce a flow-based multi-target tracker, that exploits flow consistency to filter and refine resulting tracks. The emerged precise pseudo-labels then serve as a basis for model re-training. Starting with a pre-trained KITTI model, we conduct experiments on the challenging Waymo Open Dataset to demonstrate the effectiveness of our approach. Without any prior target domain knowledge, our results show a significant improvement over the state-of-the-art.
翻译:在自主驾驶领域,自我训练被广泛应用,以减缓以利达AR为基础的三维天体探测器的分布变化。这就消除了在环境变化(例如地理位置、传感器设置、天气条件)时需要昂贵的高质量标签的必要性。然而,在自主驾驶领域,最先进的自我训练方法大多忽视了自主驾驶数据的时间性质。为解决这一问题,我们提议了一种流动自训练方法,使三维天体探测器在连续的利达AR点云上能够不受监督地对域进行调适。为了获得可靠的假标签,我们利用场景流动来传播探测结果。特别是,我们引入了一种基于流动的多目标追踪器,利用流动的一致性来过滤和完善由此形成的轨道。现在出现的精确的假标签作为模式再培训的基础。从经过预先训练的KITTI模型开始,我们在具有挑战性的Waymo Open数据集上进行实验,以证明我们的方法的有效性。没有事先的目标域知识,我们的结果显示,我们的状态已经大大改进了。