Advances in LiDAR sensors provide rich 3D data that supports 3D scene understanding. However, due to occlusion and signal miss, LiDAR point clouds are in practice 2.5D as they cover only partial underlying shapes, which poses a fundamental challenge to 3D perception. To tackle the challenge, we present a novel LiDAR-based 3D object detection model, dubbed Behind the Curtain Detector (BtcDet), which learns the object shape priors and estimates the complete object shapes that are partially occluded (curtained) in point clouds. BtcDet first identifies the regions that are affected by occlusion and signal miss. In these regions, our model predicts the probability of occupancy that indicates if a region contains object shapes. Integrated with this probability map, BtcDet can generate high-quality 3D proposals. Finally, the probability of occupancy is also integrated into a proposal refinement module to generate the final bounding boxes. Extensive experiments on the KITTI Dataset and the Waymo Open Dataset demonstrate the effectiveness of BtcDet. Particularly, for the 3D detection of both cars and cyclists on the KITTI benchmark, BtcDet surpasses all of the published state-of-the-art methods by remarkable margins. Code is released (https://github.com/Xharlie/BtcDet}{https://github.com/Xharlie/BtcDet).

翻译：LiDAR 传感器的进步提供了丰富的 3D 数据,支持对 3D 场景的理解。 但是,由于隐蔽和信号缺失, LiDAR 点云实际上只有2.5D,因为它们只覆盖部分基本形状,对 3D 感知构成根本挑战。为了应对这一挑战,我们提出了一个基于 3D 的基于 3D 的 3D 对象探测模型, 以Curtain 探测器( BtcDet) 的背后为标签, 这个模型学习了天体形状的前缀, 并估计了点云中部分隐蔽( 覆盖) 的完整对象形状。 BtcDet首先确定了受隐蔽和信号缺失影响的区域。 在这些区域, 我们的模型预测了如果一个区域含有对象形状, 则显示占用概率概率的概率。 BtcDDet可以产生高质量的 3D 提议。 最后, 占用的可能性也被纳入一个提案的精细化模块, 以生成最终的绑定框。 KITTITIT 数据集和Wemo Op 数据集展示了 Bt Dect. dect. developt.