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 diversity-based 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. To overcome this limitation, we jointly investigate three novel criteria in our framework Crb for point cloud acquisition - label conciseness}, feature representativeness and geometric balance, which hierarchically filters out the point clouds of redundant 3D bounding box labels, latent features and geometric characteristics (e.g., point cloud density) from the unlabeled sample pool and greedily selects informative ones with fewer objects to annotate. Our theoretical analysis demonstrates that the proposed criteria align 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. To validate the effectiveness and applicability of Crb, we conduct extensive experiments on the two benchmark 3D object detection datasets of KITTI and Waymo and examine both one-stage (i.e., Second) and two-stage 3D detectors (i.e., Pv-rcnn). 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. Source code: https://github.com/Luoyadan/CRB-active-3Ddet.
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving
I2R, A-STAR（新加坡科技研究局资讯通信研究院）；South China University of Technology（华南理工大学）；Southwest Jiaotong University（西南交通大学）；Chengdu University of Information Technology（成都信息工程大学）
Exploring Spatial Intelligence from a Generative Perspective
Zhejiang University（浙江大学）；State Key Laboratory of CAD & CG（计算机辅助设计与图形学国家重点实验室）；Ant Group（蚂蚁集团）；Westlake University（西湖大学）；Zhejiang University of Technology（浙江工业大学）
Exploring Cognitive Paradoxes in Video Games: A Quantum Mechanical Perspective
Artificial Intelligence and Cyber Futures Institute, Charles Sturt University（查尔斯斯特大学人工智能与网络未来研究所）；The Alan Turing Institute（阿兰·图灵研究所）；The University of Sydney Business School（悉尼大学商学院）
