# Achieving RGB-D level Segmentation Performance from a Single ToF Camera

Source: [Achieving RGB-D level Segmentation Performance from a Single ToF Camera](<https://www.sota2.com/research/paper/68fa6fee1e209131cea4836c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:30:17.231Z

Authors: Pranav Sharma, Jigyasa Singh Katrolia, Jason Rambach, Bruno Mirbach, Didier Stricker, Juergen Seiler

Published: 2023-06-30T13:14:27.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2306.17636>)

[PDF](<https://arxiv.org/pdf/2306.17636.pdf>)