期刊
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
卷 4, 期 3, 页码 298-310出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCI.2018.2849326
关键词
Coded aperture; phase mask; depth reconstruction; deep learning; computational camera
资金
- NVIDIA
- ERC-StG SPADE
- ERC-StG RAPID
Depth estimation from a single image is a well-known challenge in computer vision. With the advent of deep learning, several approaches for monocular depth estimation have been proposed, all of which have inherent limitations due to the scarce depth cues that exist in a single image. Moreover, these methods are very demanding computationally, which makes them inadequate for systems with limited processing power. In this paper, a phase-coded aperture camera for depth estimation is proposed. The camera is equipped with an optical phase mask that provides unambiguous depth-related color characteristics for the captured image. These are used for estimating the scene depth map using a fully convolutional neural network. The phase-coded aperture structure is learned jointly with the network weights using backpropagation. The strong depth cues (encoded in the image by the phase mask, designed together with the network weights) allow a much simpler neural network architecture for faster and more accurate depth estimation. Performance achieved on simulated images as well as on a real optical setup is superior to the state-of-the-art monocular depth estimation methods (both with respect to the depth accuracy and required processing power), and is competitive with more complex and expensive depth estimation methods such as light-field cameras.
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