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Reducing the quantity of annotations required for supervised training isvital when labels are scarce and costly. This reduction is especially importantfor semantic segmentation tasks involving 3D datasets that are oftensignificantly smaller and more challenging to annotate than their image-basedcounterparts. Self-supervised pre-training on large unlabelled datasets is oneway to reduce the amount of manual annotations needed. Previous work hasfocused on pre-training with point cloud data exclusively; this approach oftenrequires two or more registered views. In the present work, we combine imageand point cloud modalities, by first learning self-supervised image featuresand 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 onlyrequires a single scan of a scene. We demonstrate that our pre-trainingapproach, despite using single scans, achieves comparable performance to othermulti-scan, point cloud-only methods.

Self-Supervised Pre-training of 3D Point Cloud Networks with Image Data