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With the recent growth of urban mapping and autonomous driving efforts, therehas been an explosion of raw 3D data collected from terrestrial platforms withlidar scanners and color cameras. However, due to high labeling costs,ground-truth 3D semantic segmentation annotations are limited in both quantityand geographic diversity, while also being difficult to transfer acrosssensors. In contrast, large image collections with ground-truth semanticsegmentations are readily available for diverse sets of scenes. In this paper,we investigate how to use only those labeled 2D image collections to supervisetraining 3D semantic segmentation models. Our approach is to train a 3D modelfrom pseudo-labels derived from 2D semantic image segmentations using multiviewfusion. We address several novel issues with this approach, including how toselect trusted pseudo-labels, how to sample 3D scenes with rare objectcategories, and how to decouple input features from 2D images frompseudo-labels during training. The proposed network architecture, 2D3DNet,achieves significantly better performance (+6.2-11.4 mIoU) than baselinesduring experiments on a new urban dataset with lidar and images captured in 20cities across 5 continents.

Learning 3D Semantic Segmentation with only 2D Image Supervision