DeepToF: Off-the-Shelf Real-Time Correction of Multipath Interference in Time-of-Flight Imaging
Julio Marco, Quercus Hernandez, Adolfo Mu\~noz, Yue Dong, Adrian, Jarabo, Min H. Kim, Xin Tong, Diego Gutierrez

TL;DR
DeepToF introduces a real-time, hardware-free neural network-based method to correct multipath interference in ToF cameras, significantly improving depth accuracy without additional hardware or costly computations.
Contribution
The paper presents a novel autoencoder-based approach for MPI correction that requires no camera modifications and operates in 10 milliseconds per frame.
Findings
Effective MPI correction on real and synthetic data
No hardware modifications needed
Real-time processing capability
Abstract
Time-of-flight (ToF) imaging has become a widespread technique for depth estimation, allowing affordable off-the-shelf cameras to provide depth maps in real time. However, multipath interference (MPI) resulting from indirect illumination significantly degrades the captured depth. Most previous works have tried to solve this problem by means of complex hardware modifications or costly computations. In this work we avoid these approaches, and propose a new technique that corrects errors in depth caused by MPI that requires no camera modifications, and corrects depth in just 10 milliseconds per frame. By observing that most MPI information can be expressed as a function of the captured depth, we pose MPI removal as a convolutional approach, and model it using a convolutional neural network. In particular, given that the input and output data present similar structure, we base our network…
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