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Title: FlatNet3D: intensity and absolute depth from single-shot lensless capture

Lensless cameras are ultra-thin imaging systems that replace the lens with a thin passive optical mask and computation. Passive mask-based lensless cameras encode depth information in their measurements for a certain depth range. Early works have shown that this encoded depth can be used to perform 3D reconstruction of close-range scenes. However, these approaches for 3D reconstructions are typically optimization based and require strong hand-crafted priors and hundreds of iterations to reconstruct. Moreover, the reconstructions suffer from low resolution, noise, and artifacts. In this work, we proposeFlatNet3D—a feed-forward deep network that can estimate both depth and intensity from a single lensless capture. FlatNet3D is an end-to-end trainable deep network that directly reconstructs depth and intensity from a lensless measurement using an efficient physics-based 3D mapping stage and a fully convolutional network. Our algorithm is fast and produces high-quality results, which we validate using both simulated and real scenes captured using PhlatCam.

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Award ID(s):
1730574 1652633
NSF-PAR ID:
10372725
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
Optical Society of America
Date Published:
Journal Name:
Journal of the Optical Society of America A
Volume:
39
Issue:
10
ISSN:
1084-7529; JOAOD6
Page Range / eLocation ID:
Article No. 1903
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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