The annotation of 3D datasets is required for semantic-segmentation and object detection in scene understanding. In this paper we present a framework for the weakly supervision of a point clouds transformer that is used for 3D object detection. The aim is to decrease the required amount of supervision needed for training, as a result of the high cost of annotating a 3D datasets. We propose an Unsupervised Voting Proposal Module, which learns randomly preset anchor points and uses voting network to select prepared anchor points of high quality. Then it distills information into student and teacher network. In terms of student network, we apply ResNet network to efficiently extract local characteristics. However, it also can lose much global information. To provide the input which incorporates the global and local information as the input of student networks, we adopt the self-attention mechanism of transformer to extract global features, and the ResNet layers to extract region proposals. The teacher network supervises the classification and regression of the student network using the pre-trained model on ImageNet. On the challenging KITTI datasets, the experimental results have achieved the highest level of average precision compared with the most recent weakly supervised 3D object detectors.

翻译：三维数据集的标注是场景理解中语义分割和目标检测的必要前提。本文提出了一种用于三维目标检测的弱监督点云Transformer框架，旨在通过降低三维数据集标注的高昂成本来减少训练所需的监督量。我们提出了一种无监督投票提案模块，该模块学习随机预设锚点，并利用投票网络筛选高质量预选锚点，随后将信息蒸馏至学生网络和教师网络。在学生网络中，我们采用ResNet网络高效提取局部特征，但该方法可能丢失大量全局信息。为提供融合全局与局部信息的输入，我们采用Transformer的自注意力机制提取全局特征，并利用ResNet层提取区域提案。教师网络通过ImageNet预训练模型监督学生网络的分类与回归任务。在具有挑战性的KITTI数据集上，实验结果表明，与最新弱监督三维目标检测器相比，所提方法的平均精度达到了最高水平。