Müller Tobias, Schmähling Tobias, Elser Stefan, Eberhardt Jörg
Institute for Photonic Systems Hochschule Ravensburg-Weingarten, University of Applied Sciences, Doggenriedstraße, 88250 Weingarten, Germany.
Institute for Artificial Intelligence Hochschule Ravensburg-Weingarten, University of Applied Sciences, Doggenriedstraße, 88250 Weingarten, Germany.
J Imaging. 2024 Oct 8;10(10):246. doi: 10.3390/jimaging10100246.
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.
飞行时间（ToF）相机由于多径干扰（MPI）而受到高水平的噪声和误差影响。为了纠正这些误差，算法和神经网络需要训练数据。然而，真实数据的有限可用性导致了对物理模拟数据的使用，而物理模拟数据往往涉及简化和计算限制。此类传感器的模拟是硬件设计和应用开发的重要组成部分。因此，模拟数据必须捕捉主要的传感器特性。这项工作提出了一种基于学习的方法，该方法利用高质量的激光扫描数据来生成逼真的ToF相机数据。所提出的方法采用MCW-Net（多级连接和广域非局部块网络）进行域转换，将激光扫描数据转换到ToF相机域。使用真实世界数据集探索了不同的训练变体。此外，引入了一个噪声模型来弥补初始步骤中噪声的不足。在参考场景上评估该方法的有效性，以便与物理模拟数据进行定量比较。
