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.

本文提出了一种用于 3D 物体检测的弱监督点云变换器框架，旨在降低对训练所需监督的数量，以减少对 3D 数据集进行注释的高成本，通过使用投票网络选择高质量的预设锚点，将信息提炼到学生网络和教师网络。