Achieving RGB-D level Segmentation Performance from a Single ToF Camera
Pranav Sharma Jigyasa Singh Katrolia Jason Rambach Bruno Mirbach Didier Stricker Juergen Seiler
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.