Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 27, Issue 8, Pages 4131-4144Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2018.2836318
Keywords
Depth estimation; 2D-to-3D conversion; non-parametric sampling; convolutional neural networks; RGB-D database
Funding
- National Research Foundation of Korea (NRF), Ministry of Science, ICT [NRF-2017M3C4A7069370]
- NRF [NRF-2015R1D1A1A01061143]
- National Research Foundation of Korea [2015R1D1A1A01061143, 2017M3C4A7069370] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Recent works on machine learning have greatly advanced the accuracy of single image depth estimation. However, the resulting depth images are still over-smoothed and perceptually unsatisfying. This paper casts depth prediction from single image as a parametric learning problem. Specifically, we propose a deep variational model that effectively integrates heterogeneous predictions from two convolutional neural networks (CNNs), named global and local networks. They have contrasting network architecture and are designed to capture the depth information with complementary attributes. These intermediate outputs are then combined in the integration network based on the variational framework. By unrolling the optimization steps of Split Bregman iterations in the integration network, our model can be trained in an end-to-end manner. This enables one to simultaneously learn an efficient parameterization of the CNNs and hyper-parameter in the variational method. Finally, we offer a new data set of 0.22 million RGB-D images captured by Microsoft Kinect v2. Our model generates realistic and discontinuity-preserving depth prediction without involving any low-level segmentation or superpixels. Intensive experiments demonstrate the superiority of the proposed method in a range of RGB-D benchmarks, including both indoor and outdoor scenarios.
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