360$^\circ$ from a Single Camera: A Few-Shot Approach for LiDAR
  Segmentation
Laurenz Reichardt Nikolas Ebert Oliver Wasenmüller
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.