Article
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Deep Learning for Generating Time-of-Flight Camera Artifacts
Version 1
: Received: 6 August 2024 / Approved: 7 August 2024 / Online: 7 August 2024 (17:57:53 CEST)
How to cite: Müller, T.; Schmähling, T.; Elser, S.; Eberhardt, J. Deep Learning for Generating Time-of-Flight Camera Artifacts. Preprints 2024, 2024080483. https://doi.org/10.20944/preprints202408.0483.v1 Müller, T.; Schmähling, T.; Elser, S.; Eberhardt, J. Deep Learning for Generating Time-of-Flight Camera Artifacts. Preprints 2024, 2024080483. https://doi.org/10.20944/preprints202408.0483.v1
Abstract
Time-of-Flight (ToF) cameras are subject to high levels of noise and errors due to Multi-Path-Interference (MPI). To correct these errors, algorithms and neuronal networks require training data. However, the limited availability of real data has led to the use of physically simulated data, which often involves simplifications and computational constraints. The simulation of such sensors is an essential building block for hardware design and application development. Therefore, the simulation data must capture the major sensor characteristics. This work presents a learning-based approach that leverages high-quality laser scan data to generate realistic ToF camera data. The proposed method employs MCW-Net (Multi-Level Connection and Wide Regional Non-Local Block Network) for domain transfer, transforming laser scan data into the ToF camera domain. Different training variations are explored using a real-world dataset. Additionally, a noise model is introduced to compensate for the lack of noise in the initial step. The effectiveness of the method is evaluated on reference scenes to quantitatively compare to physically simulated data.
Keywords
Time-of-flight; learning-based simulation; domain transfer
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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