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.

通过使用 RGB 图像提供场景的更密集表示，我们提出了一种基于图像引导网络（IGNet）的方法，该方法借鉴了通过域适应合成训练的 2D 语义分割网络中的高级特征信息，进一步利用了一种称为 FOVMix 的新型混合策略以解决两个传感器之间的水平视场不匹配问题，以增强图像引导效果，并在 ScribbleKITTI 上实现了弱监督 LiDAR 语义分割的最先进结果，与全监督训练相比，仅使用 8% 的标记点，无需额外的标注负担或推理中的计算 / 内存成本。此外，我们还展示了我们的方法在半监督训练中的有效性，IGNet 在 ScribbleKITTI 和 SemanticKITTI 上均取得了最先进的结果。