Deep learning applications on LiDAR data suffer from a strong domain gap when
applied to different sensors or tasks. In order for these methods to obtain
similar accuracy on different data in comparison to values reported on public
benchmarks, a large scale annotated dataset is necessary. However, in practical
applications labeled data is costly and time consuming to obtain. Such factors
have triggered various research in label-efficient methods, but a large gap
remains to their fully-supervised counterparts. Thus, we propose ImageTo360, an
effective and streamlined few-shot approach to label-efficient LiDAR
segmentation. Our method utilizes an image teacher network to generate semantic
predictions for LiDAR data within a single camera view. The teacher is used to
pretrain the LiDAR segmentation student network, prior to optional fine-tuning
on 360$^\circ$ data. Our method is implemented in a modular manner on the point
level and as such is generalizable to different architectures. We improve over
the current state-of-the-art results for label-efficient methods and even
surpass some traditional fully-supervised segmentation networks.

我们提出了 ImageTo360，一种有效且简化的少样本标签高效 LiDAR 分割方法，利用图像教师网络在单个摄像机视角下为 LiDAR 数据生成语义预测，并使 LiDAR 分割学生网络进行预训练，从而改进了当前标签高效方法的结果并超越了一些传统的全监督分割网络。