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 training framework called SurgDepth%2C and show state-of-the-art (SOTA) results on all publicly available datasets applicable for this task. Unlike previous approaches%2C which either fine-tune SOTA segmentation models trained on natural images%2C or encode RGB or RGB-D information using RGB only pre-trained backbones%2C SurgDepth%2C which is built on top of Vision Transformers (ViTs)%2C is designed to encode both RGB and depth information through a simple fusion mechanism. We conduct extensive experiments on benchmark datasets including EndoVis2022%2C AutoLapro%2C LapI2I and EndoVis2017 to verify the efficacy of SurgDepth. Specifically%2C SurgDepth achieves a new SOTA IoU of 0.86 on EndoVis 2022 SAR-RARP50 challenge and outperforms the current best method by at least 4%25%2C using a shallow and compute efficient decoder consisting of ConvNeXt blocks.?quality=80&w=800)
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