PhaseCam3D — Learning Phase Masks for Passive Single View Depth Estimation
PhaseCam3D — Learning Phase Masks for Passive Single View Depth Estimation
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DOI：
10.1109/iccphot.2019.8747330
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Yichen Wu;Vivek Boominathan;Huaijin Chen;Aswin C. Sankaranarayanan;A. Veeraraghavan
中科院分区：
文献类型：
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作者：
Yichen Wu;Vivek Boominathan;Huaijin Chen;Aswin C. Sankaranarayanan;A. Veeraraghavan
在许多具有严格能量限制的应用中，对被动3D扫描的需求越来越大。本文提出了一种利用相位掩模在相机孔径平面上实现单帧、单视点被动三维成像的方法。我们的方法依赖于一个端到端优化框架来共同学习最优相位掩模和重建算法，该算法允许从捕获的数据中准确估计距离图像。利用我们的优化框架，我们设计了一个新的相位掩模，其性能明显优于现有的方法。我们将采用光刻技术制作的相位掩模插入到传统相机的孔径平面中，并在3D成像中显示出令人信服的性能。
There is an increasing need for passive 3D scanning in many applications that have stringent energy constraints. In this paper, we present an approach for single frame, single viewpoint, passive 3D imaging using a phase mask at the aperture plane of a camera. Our approach relies on an end-to-end optimization framework to jointly learn the optimal phase mask and the reconstruction algorithm that allows an accurate estimation of range image from captured data. Using our optimization framework, we design a new phase mask that performs significantly better than existing approaches. We build a prototype by inserting a phase mask fabricated using photolithography into the aperture plane of a conventional camera and show compelling performance in 3D imaging.