Energy-based Active Learning for Bringing Beam-induced Domain Gap for 3D Object Detection

In many real-world applications, 16-beam LiDAR-based 3D object detection (3DOD) is indispensable in scene understanding. However, the absence of well-labeled large-scale 16-beam LiDAR datasets impedes the development of these 3DOD methods. To avoid annotation costs in developing datasets, we proposed an energy-based active learning method for cross-beam domain adaptation, which effectively transfers the knowledge from the existing well-labeled 64-beam counterpart. Specifically, the cross-beam domain gap between the source (64-beam) and the target (16-beam) domain is reduced by aligning the deep features based on an energy-based feature-matching loss term during training. Moreover, the proposed energy-based active learning method enables the sampling strategy to shed light on selecting the most valuable 16-beam target samples to be manually labeled, which are then added to the training set. Experimental results show that our method can effectively transfer the knowledge from the 64-beam domain to the 16-beam one, and successfully learns a high-performance 16-beam 3DOD model with only a small portion of unlabeled data to annotate.