论文标题
标签引导的辅助培训改善了3D对象检测器
Label-Guided Auxiliary Training Improves 3D Object Detector
论文作者
论文摘要
从点云中检测3D对象是一项实用但充满挑战的任务，最近引起了越来越多的关注。在本文中，我们提出了用于3D对象检测（LG3D）的标签引导辅助训练方法，该方法是增强现有3D对象检测器的特征学习的辅助网络。具体而言，我们提出了两个新型模块：一个标签 - 通道诱导器，将边界框中的注释和点云映射到特定于任务的表示形式和一个标签知识 - 贴纸，该标签 - 知识映射器有助于原始特征以获取检测临界表示。提出的辅助网络被推理丢弃，因此在测试时没有额外的计算成本。我们在室内和室外数据集上进行了广泛的实验，以验证方法的有效性。例如，我们拟议的LG3D分别在SUN RGB-D和SCANNETV2数据集上将投票人员分别提高了2.5％和3.1％的地图。
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.
