3-D object detection is pivotal for autonomous driving. Point cloud based methods have become increasingly popular for 3-D object detection, owing to their accurate depth information. NuTonomy's nuScenes dataset greatly extends commonly used datasets such as KITTI in size, sensor modalities, categories, and annotation numbers. However, it suffers from severe class imbalance. The Class-balanced Grouping and Sampling paper addresses this issue and suggests augmentation and sampling strategy. However, the localization precision of this model is affected by the loss of spatial information in the downscaled feature maps. We propose to enhance the performance of the CBGS model by designing an auxiliary network, that makes full use of the structure information of the 3D point cloud, in order to improve the localization accuracy. The detachable auxiliary network is jointly optimized by two point-level supervisions, namely foreground segmentation and center estimation. The auxiliary network does not introduce any extra computation during inference, since it can be detached at test time.

翻译：三维天体探测对于自主驱动至关重要。 点云基方法由于精确的深度信息,对三维天体探测越来越受欢迎。 努托诺米的 nuSenes 数据集在大小、 传感器模式、 类别和注解数方面大大扩展了常用数据集, 如 KITTI 的大小、 传感器模式、 类别和批注数。 但是, 它存在严重的阶级不平衡。 分类平衡的组和取样文件解决这个问题, 并提出了增强和取样战略。 但是, 该模型的本地化精确度受到低尺度地貌图中空间信息丢失的影响。 我们提议通过设计辅助网络来提高 CBGS 模型的性能, 充分利用 3D 点云的结构信息, 以提高本地化精确度。 可拆分的辅助网络由两个点级监督单位共同优化, 即地段和中心估计。 辅助网络在推断过程中不会引入任何额外的计算方法, 因为测试时间可以分离 。