OpenBox: Annotate Any Bounding Boxes in 3D
In-Jae Lee, Mungyeom Kim, Kwonyoung Ryu, Pierre Musacchio, Jaesik Park
Unsupervised and open-vocabulary 3D object detection has recently gained attention, particularly in autonomous driving, where reducing annotation costs and recognizing unseen objects are critical for both safety and scalability. However, most existing approaches apply designated 3D object detectors and require multiple iterations of self-training for pseudo-ground truth refinement, leading to substantial computational overhead. To address these challenges, we propose OpenBox, a two-stage automatic annotation pipeline that leverages a 2D visual foundation model. In the first stage, OpenBox associates instance-level information from 2D images processed by a visual foundation model with the corresponding 3D point cloud. In the second stage, it categorizes the instances by rigidity and motion state, and generates adaptive bounding boxes using class-specific size statistics. As a result, OpenBox can generate high-quality annotations without requiring self-training. Experiments on the Waymo and Lyft datasets demonstrate improved accuracy and efficiency compared to baselines.