  Reducing the quantity of annotations required for supervised training is
vital when labels are scarce and costly. This reduction is particularly
important for semantic segmentation tasks involving 3D datasets, which are
often significantly smaller and more challenging to annotate than their
image-based counterparts. Self-supervised pre-training on unlabelled data is
one way to reduce the amount of manual annotations needed. Previous work has
focused on pre-training with point clouds exclusively. While useful, 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 and can be applied
to cases where localization information is unavailable. We demonstrate that our
pre-training approach, despite using single scans, achieves comparable
performance to other multi-scan, point cloud-only methods.
