Exploring Active 3D Object Detection from a Generalization Perspective

To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is a promising solution that learns to select only a small portion of unlabeled data to annotate, without compromising model performance. Our empirical study, however, suggests that mainstream uncertainty-based and diversitybased active learning policies are not effective when applied in the 3D detection task, as they fail to balance the trade-off between point cloud informativeness and box-level annotation costs. Our theoretical analysis demonstrates that the proposed criteria aligns the marginal distributions of the selected subset and the prior distributions of the unseen test set, and minimizes the upper bound of the generalization error. Experiments evidence that the proposed approach outperforms existing active learning strategies and achieves fully supervised performance requiring 1% and 8% annotations of bounding boxes and point clouds, respectively. LiDAR-based 3D object detection plays an indispensable role in 3D scene understanding with a wide range of applications such as autonomous driving (Deng et al., 2021; Wang et al., 2020) and robotics (Ahmed et al., 2018; Montes et al., 2020; Wang et al., 2019). The emerging stream of 3D detection models enables accurate recognition at the cost of large-scale labeled point clouds, where 7-degree of freedom (DOF) 3D bounding boxes - consisting of a position, size, and orientation informationfor each object are annotated. In the benchmark datasets like Waymo (Sun et al., 2020), there are over 12 million LiDAR boxes, for which, labeling a precise 3D box takes more than 100 seconds for an annotator (Song et al., 2015). This prerequisite for the performance boost greatly hinders the feasibility of applying models to the wild, especially when the annotation budget is limited. To alleviate this limitation, active learning (AL) aims to reduce labeling costs by querying labels for only a small portion of unlabeled data. The criterion-based query selection process iteratively selects the most beneficial samples for the subsequent model training until the labeling budget is run out.