  As 3D perception problems grow in popularity and the need for large-scale
labeled datasets for LiDAR semantic segmentation increase, new methods arise
that aim to reduce the necessity for dense annotations by employing
weakly-supervised training. However these methods continue to show weak
boundary estimation and high false negative rates for small objects and distant
sparse regions. We argue that such weaknesses can be compensated by using RGB
images which provide a denser representation of the scene. We propose an
image-guidance network (IGNet) which builds upon the idea of distilling high
level feature information from a domain adapted synthetically trained 2D
semantic segmentation network. We further utilize a one-way contrastive
learning scheme alongside a novel mixing strategy called FOVMix, to combat the
horizontal field-of-view mismatch between the two sensors and enhance the
effects of image guidance. IGNet achieves state-of-the-art results for
weakly-supervised LiDAR semantic segmentation on ScribbleKITTI, boasting up to
98% relative performance to fully supervised training with only 8% labeled
points, while introducing no additional annotation burden or
computational/memory cost during inference. Furthermore, we show that our
contributions also prove effective for semi-supervised training, where IGNet
claims state-of-the-art results on both ScribbleKITTI and SemanticKITTI.
