Title
PhaseCam3D — Learning Phase Masks for Passive Single View Depth Estimation
Abstract
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.
Year
DOI
Venue
2019
10.1109/ICCPHOT.2019.8747330
2019 IEEE International Conference on Computational Photography (ICCP)
Keywords
Field
DocType
computational photography,passive depth estimation,coded aperture,phase masks
Aperture,Computer vision,Coded aperture,Computer science,Computational photography,Photolithography,Reconstruction algorithm,Artificial intelligence
Conference
ISSN
ISBN
Citations 
2164-9774
978-1-7281-3264-8
7
PageRank 
References 
Authors
0.42
21
5
Name
Order
Citations
PageRank
yicheng wu1549.19
Vivek Boominathan2283.82
Huaijin Chen3192.44
Aswin C. Sankaranarayanan477051.51
Ashok Veeraraghavan5149588.93