  In this work, we leverage estimated depth to boost self-supervised
contrastive learning for segmentation of urban scenes, where unlabeled videos
are readily available for training self-supervised depth estimation. We argue
that the semantics of a coherent group of pixels in 3D space is self-contained
and invariant to the contexts in which they appear. We group coherent,
semantically related pixels into coherent depth regions given their estimated
depth and use copy-paste to synthetically vary their contexts. In this way,
cross-context correspondences are built in contrastive learning and a
context-invariant representation is learned. For unsupervised semantic
segmentation of urban scenes, our method surpasses the previous
state-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 competitive
with existing models, yet, we do not need to pre-train on ImageNet or COCO, and
we are also more computationally efficient. Our code is available on
https://github.com/LeungTsang/CPCDR
