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Deep Learning for Generating Time-of-Flight Camera Artifacts.

Tobias Müller¹, Tobias Schmähling¹, Stefan Elser²

¹Institute for Photonic Systems Hochschule Ravensburg-Weingarten, University of Applied Sciences, Doggenriedstraße, 88250 Weingarten, Germany.

Journal of Imaging

|October 25, 2024

Summary

This summary is machine-generated.

This study introduces a novel learning-based method using MCW-Net to generate realistic Time-of-Flight (ToF) camera data from laser scans. This approach enhances sensor simulation by incorporating a noise model for improved accuracy.

Keywords:

domain transfer learning-based simulation time-of-flight

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Area of Science:

Computer Vision
Robotics
Sensor Simulation

Background:

Time-of-Flight (ToF) cameras suffer from Multi-Path Interference (MPI) noise and errors.
Acquiring sufficient real-world training data for ToF error correction is challenging.
Existing physically simulated data often lacks critical sensor characteristics due to simplifications.

Purpose of the Study:

To develop a learning-based approach for generating realistic ToF camera data.
To overcome limitations of current simulated data for ToF sensor development.
To improve the accuracy and applicability of ToF sensor simulations.

Main Methods:

Leveraging high-quality laser scan data as input.
Employing MCW-Net (Multi-Level Connection and Wide Regional Non-Local Block Network) for domain transfer.
Integrating a noise model to simulate realistic sensor noise.
Exploring various training strategies with real-world datasets.

Main Results:

Successful transformation of laser scan data into realistic ToF camera data.
Demonstrated effectiveness of the MCW-Net approach for domain adaptation.
Quantitative evaluation on reference scenes showing improved realism compared to traditional simulations.

Conclusions:

The proposed learning-based method effectively generates high-fidelity ToF camera data.
This technique enhances the utility of simulated data for ToF camera hardware design and application development.
The approach offers a viable solution for data scarcity in ToF sensor research.