DFormer++: Improving RGBD Representation Learning for Semantic Segmentation.

Abstract

We explore the potential of pretrain-and-finetune manner on the RGB-D semantic segmentation to solve the common mismatch problem in this field. Specifically, we present DFormer++, a novel RGB-D pretrain-and-finetune framework to learn transferable representations for RGB-D semantic segmentation. This paper has two vital innovations. 1) Framework perspective: Different from the existing methods that finetune RGB pretrained backbone to the RGB-D scenes, we pretrain the backbone using image-depth pairs from ImageNet-1K, and hence the model is endowed with the capacity to encode RGB-D representations; 2) Architecture perspective: Our model comprises a sequence of RGB-D attention blocks, which are tailored for encoding both RGB and depth information through a novel attention mechanism. Our DFormer++ avoids the mismatched encoding of the 3D geometry relationships in depth maps by RGB pretrained backbones, which widely lies in previous works but has not been resolved. Meanwhile, the tailored architecture greatly reduces redundant parameters for encoding RGB-D data and achieves efficient and accurate perception. Experimental results show that our DFormer++ achieves new cutting-edge performance on three popular RGB-D semantic segmentation benchmarks. Our code is available at: https://github.com/VCIP-RGBD/DFormer.