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In this work, we leverage estimated depth to boost self-supervisedcontrastive learning for segmentation of urban scenes, where unlabeled videosare readily available for training self-supervised depth estimation. We arguethat the semantics of a coherent group of pixels in 3D space is self-containedand invariant to the contexts in which they appear. We group coherent,semantically related pixels into coherent depth regions given their estimateddepth and use copy-paste to synthetically vary their contexts. In this way,cross-context correspondences are built in contrastive learning and acontext-invariant representation is learned. For unsupervised semanticsegmentation of urban scenes, our method surpasses the previousstate-of-the-art baseline by +7.14% in mIoU on Cityscapes and +6.65% on KITTI.For fine-tuning on Cityscapes and KITTI segmentation, our method is competitivewith existing models, yet, we do not need to pre-train on ImageNet or COCO, andwe are also more computationally efficient. Our code is available onhttps://github.com/LeungTsang/CPCDR

Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene Segmentation