Detecting 3D objects from point clouds is a practical yet challenging task
that has attracted increasing attention recently. In this paper, we propose a
Label-Guided auxiliary training method for 3D object detection (LG3D), which
serves as an auxiliary network to enhance the feature learning of existing 3D
object detectors. Specifically, we propose two novel modules: a
Label-Annotation-Inducer that maps annotations and point clouds in bounding
boxes to task-specific representations and a Label-Knowledge-Mapper that
assists the original features to obtain detection-critical representations. The
proposed auxiliary network is discarded in inference and thus has no extra
computational cost at test time. We conduct extensive experiments on both
indoor and outdoor datasets to verify the effectiveness of our approach. For
example, our proposed LG3D improves VoteNet by 2.5% and 3.1% mAP on the SUN
RGB-D and ScanNetV2 datasets, respectively.

本文提出了一种用于改善现有 3D 目标检测器的特征学习的标签指导辅助训练方法，该方法包括两个模块：一个用于将注释和点云映射到特定任务表示的标签注释诱导器和一个协助原始特征获取检测关键表示的标签知识映射器。在室内和室外数据集上的广泛实验验证了该方法的有效性。