论文标题
du $^2 $ net：从双面包车和双像素的学习深度估计
Du$^2$Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
论文作者
论文摘要
计算立体声已经达到了高度的准确性，但是在沿边缘的遮挡，重复纹理和对应误差的情况下降低了。我们提出了一种基于神经网络的新方法，以进行深度估计，该方法将双摄像头的立体声与双像素传感器的立体声结合在一起，这在消费者摄像机上越来越普遍。我们的网络使用新颖的体系结构来融合这两个信息来源，并可以克服纯双眼立体匹配的上述局限性。我们的方法提供了一个茂密的深度图，并具有锋利的边缘，这对于诸如合成浅深度或3D照片之类的计算摄影应用至关重要。此外，我们通过设计立体声基线与双像素基线正交，避免由于立体声摄像机的光圈问题而引起的固有歧义。我们介绍了与最先进的方法的实验和比较，以表明我们的方法比以前的作品有了很大的改进。
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.
