Depth is a very important modality in computer vision, typically used as complementary information to RGB, provided by RGB-D cameras. In this work, we show that it is possible to obtain the same level of accuracy as RGB-D cameras on a semantic segmentation task using infrared (IR) and depth images from a single Time-of-Flight (ToF) camera. In order to fuse the IR and depth modalities of the ToF camera, we introduce a method utilizing depth-specific convolutions in a multi-task learning framework. In our evaluation on an in-car segmentation dataset, we demonstrate the competitiveness of our method against the more costly RGB-D approaches.
@article{arxiv.2306.17636,
title = {Achieving RGB-D level Segmentation Performance from a Single ToF Camera},
author = {Pranav Sharma and Jigyasa Singh Katrolia and Jason Rambach and Bruno Mirbach and Didier Stricker and Juergen Seiler},
journal= {arXiv preprint arXiv:2306.17636},
year = {2023}
}