We consider the problem of cross-sensor domain adaptation in the context of LiDAR-based 3D object detection and propose Stationary Object Aggregation Pseudo-labelling (SOAP) to generate high quality pseudo-labels for stationary objects. In contrast to the current state-of-the-art in-domain practice of aggregating just a few input scans, SOAP aggregates entire sequences of point clouds at the input level to reduce the sensor domain gap. Then, by means of what we call quasi-stationary training and spatial consistency post-processing, the SOAP model generates accurate pseudo-labels for stationary objects, closing a minimum of 30.3% domain gap compared to few-frame detectors. Our results also show that state-of-the-art domain adaptation approaches can achieve even greater performance in combination with SOAP, in both the unsupervised and semi-supervised settings.

翻译：我们考虑基于LiDAR的三维目标检测中跨传感器域自适应问题，并提出静止物体聚合伪标记（Stationary Object Aggregation Pseudo-labelling, SOAP）方法，为静止物体生成高质量伪标签。与当前仅聚合少量输入扫描的领域内最优实践相反，SOAP在输入层面聚合整个点云序列以缩小传感器域差距。随后，通过我们提出的准静态训练与空间一致性后处理，SOAP模型能够生成静止物体的精准伪标签，相比少帧检测器至少缩小30.3%的域差距。研究结果还表明，在无监督和半监督场景下，最先进的域自适应方法与SOAP结合后可取得更优性能。