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.

本文提出了一种基于神经网络的方法，将双目摄像机的立体匹配与双像素传感器的立体匹配相结合，以实现精准的景深图像，避免了前人研究中存在的模糊和匹配错误等问题，该方法对计算机摄影应用具有重要意义。