Du$^2$Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels

• URL: http://arxiv.org/abs/2003.14299v1
• Date: Tue, 31 Mar 2020 15:39:43 GMT
• Title: Du$^2$Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
• Authors: Yinda Zhang, Neal Wadhwa, Sergio Orts-Escolano, Christian H\"ane, Sean Fanello, and Rahul Garg
• Abstract summary: We present a novel approach based on neural networks for depth estimation that combines stereo from dual cameras with stereo from a dual-pixel sensor. Our network uses a novel architecture to fuse these two sources of information and can overcome the limitations of pure binocular stereo matching.
• Score: 16.797169907541164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges. We present a novel approach based on neural networks for depth estimation that combines stereo from dual cameras with stereo from a dual-pixel sensor, which is increasingly common on consumer cameras. Our network uses a novel architecture to fuse these two sources of information and can overcome the above-mentioned limitations of pure binocular stereo matching. Our method provides a dense depth map with sharp edges, which is crucial for computational photography applications like synthetic shallow-depth-of-field or 3D Photos. Additionally, we avoid the inherent ambiguity due to the aperture problem in stereo cameras by designing the stereo baseline to be orthogonal to the dual-pixel baseline. We present experiments and comparisons with state-of-the-art approaches to show that our method offers a substantial improvement over previous works.

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