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Most approaches for semantic segmentation use only information from colorcameras to parse the scenes, yet recent advancements show that using depth dataallows to further improve performances. In this work, we focus ontransformer-based deep learning architectures, that have achievedstate-of-the-art performances on the segmentation task, and we propose toemploy depth information by embedding it in the positional encoding.Effectively, we extend the network to multimodal data without adding anyparameters and in a natural way that makes use of the strength of transformers'self-attention modules. We also investigate the idea of performingcross-modality operations inside the attention module, swapping the key inputsbetween the depth and color branches. Our approach consistently improvesperformances on the Cityscapes benchmark.

DepthFormer: Multimodal Positional Encodings and Cross-Input Attention for Transformer-Based Segmentation Networks