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DeepToF: off-the-shelf real-time correction of multipath interference in time-of-flight imaging by Julio Marco; Quercus Hernandez; Adolfo Muñoz; Yue Dong; Adrian Jarabo; Min H. Kim; Xin Tong; Diego Gutierrez is a Computer Science article available to read on EtoBox.

What is DeepToF: off-the-shelf real-time correction of multipath interference in time-of-flight imaging about?

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 to correct errors in depth caused by MPI, which requires no camera modifications and takes just 10 milliseconds per frame. Our observations about the nature of MPI suggest that most of its information is available in image space; this allows us to formulate the depth imaging process as a spatially-varying convolution and use a convolutional neural network to correct MPI errors. Since the input and output data present similar structure, we base our network on an autoencoder, which we train in two stages. First, we use the encoder (convolution filters) to learn a suitable basis to represent MPI-corrupted depth images; then, we train the decoder (deconvolution filters) to correct depth from synthetic scenes, generated by usi

Who reads DeepToF: off-the-shelf real-time correction of multipath interference in time-of-flight imaging?

Author

Julio Marco; Quercus Hernandez; Adolfo Muñoz; Yue Dong; Adrian Jarabo; Min H. Kim; Xin Tong; Diego Gutierrez

Publisher

ACM

Published

2017

Language

EN

Field

Computer Science (Physical Sciences)