Achieving RGB-D Level Segmentation Performance From a Single ToF Camera

Achieving RGB-D Level Segmentation Performance From a Single ToF Camera
Pranav Sharma, Jigyasa Singh Katrolia, Jason Raphael Rambach, Bruno Mirbach, Didier Stricker
In: Proceedings of the. International Conference on Pattern Recognition Applications and Methods (ICPRAM-2024), February 24-26, Rome, Italy, SCITEPRESS, 2/2024.

Abstract:

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.