  Reducing the quantity of annotations required for supervised training is
vital when labels are scarce and costly. This reduction is especially important
for semantic segmentation tasks involving 3D datasets that are often
significantly smaller and more challenging to annotate than their image-based
counterparts. Self-supervised pre-training on large unlabelled datasets is one
way to reduce the amount of manual annotations needed. Previous work has
focused on pre-training with point cloud data exclusively; this approach often
requires two or more registered views. In the present work, we combine image
and point cloud modalities, by first learning self-supervised image features
and then using these features to train a 3D model. By incorporating image data,
which is often included in many 3D datasets, our pre-training method only
requires a single scan of a scene. We demonstrate that our pre-training
approach, despite using single scans, achieves comparable performance to other
multi-scan, point cloud-only methods.
