Sub-picosecond photon-efficient 3D imaging using single-photon sensors

Felix Heide; Steven Diamond; David B. Lindell; Gordon Wetzstein

doi:10.1038/s41598-018-35212-x

Sub-picosecond photon-efficient 3D imaging using single-photon sensors

Felix Heide, Steven Diamond, David B. Lindell, Gordon Wetzstein

Research output: Contribution to journal › Article › peer-review

86 Scopus citations

Abstract

Active 3D imaging systems have broad applications across disciplines, including biological imaging, remote sensing and robotics. Applications in these domains require fast acquisition times, high timing accuracy, and high detection sensitivity. Single-photon avalanche diodes (SPADs) have emerged as one of the most promising detector technologies to achieve all of these requirements. However, these detectors are plagued by measurement distortions known as pileup, which fundamentally limit their precision. In this work, we develop a probabilistic image formation model that accurately models pileup. We devise inverse methods to efficiently and robustly estimate scene depth and reflectance from recorded photon counts using the proposed model along with statistical priors. With this algorithm, we not only demonstrate improvements to timing accuracy by more than an order of magnitude compared to the state-of-the-art, but our approach is also the first to facilitate sub-picosecond-accurate, photon-efficient 3D imaging in practical scenarios where widely-varying photon counts are observed.

Original language	English (US)
Journal	Scientific Reports
Volume	8
Issue number	1
DOIs	https://doi.org/10.1038/s41598-018-35212-x
State	Published - Dec 7 2018
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2021-03-10
Acknowledgements: This work was in part supported by a National Science Foundation CAREER award (IIS 1553333), by a Sloan Fellowship, by the DARPA REVEAL program, and by the KAUST Office of Sponsored Research through the Visual Computing Center CCF grant. The authors would like to thank Rafael Setra, Kai Zang, Matthew O’Toole, Amy Fritz, and Mark Horowitz for fruitful discussions in early stages of this project. S.D. was supported by a National Science Foundation GraduateResearch Fellowship and D.B.L. was supported by a Stanford Graduate Fellowship in Science and Engineering.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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Cite this

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title = "Sub-picosecond photon-efficient 3D imaging using single-photon sensors",

abstract = "Active 3D imaging systems have broad applications across disciplines, including biological imaging, remote sensing and robotics. Applications in these domains require fast acquisition times, high timing accuracy, and high detection sensitivity. Single-photon avalanche diodes (SPADs) have emerged as one of the most promising detector technologies to achieve all of these requirements. However, these detectors are plagued by measurement distortions known as pileup, which fundamentally limit their precision. In this work, we develop a probabilistic image formation model that accurately models pileup. We devise inverse methods to efficiently and robustly estimate scene depth and reflectance from recorded photon counts using the proposed model along with statistical priors. With this algorithm, we not only demonstrate improvements to timing accuracy by more than an order of magnitude compared to the state-of-the-art, but our approach is also the first to facilitate sub-picosecond-accurate, photon-efficient 3D imaging in practical scenarios where widely-varying photon counts are observed.",

author = "Felix Heide and Steven Diamond and Lindell, {David B.} and Gordon Wetzstein",

note = "KAUST Repository Item: Exported on 2021-03-10 Acknowledgements: This work was in part supported by a National Science Foundation CAREER award (IIS 1553333), by a Sloan Fellowship, by the DARPA REVEAL program, and by the KAUST Office of Sponsored Research through the Visual Computing Center CCF grant. The authors would like to thank Rafael Setra, Kai Zang, Matthew O{\textquoteright}Toole, Amy Fritz, and Mark Horowitz for fruitful discussions in early stages of this project. S.D. was supported by a National Science Foundation GraduateResearch Fellowship and D.B.L. was supported by a Stanford Graduate Fellowship in Science and Engineering. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

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doi = "10.1038/s41598-018-35212-x",

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N2 - Active 3D imaging systems have broad applications across disciplines, including biological imaging, remote sensing and robotics. Applications in these domains require fast acquisition times, high timing accuracy, and high detection sensitivity. Single-photon avalanche diodes (SPADs) have emerged as one of the most promising detector technologies to achieve all of these requirements. However, these detectors are plagued by measurement distortions known as pileup, which fundamentally limit their precision. In this work, we develop a probabilistic image formation model that accurately models pileup. We devise inverse methods to efficiently and robustly estimate scene depth and reflectance from recorded photon counts using the proposed model along with statistical priors. With this algorithm, we not only demonstrate improvements to timing accuracy by more than an order of magnitude compared to the state-of-the-art, but our approach is also the first to facilitate sub-picosecond-accurate, photon-efficient 3D imaging in practical scenarios where widely-varying photon counts are observed.

AB - Active 3D imaging systems have broad applications across disciplines, including biological imaging, remote sensing and robotics. Applications in these domains require fast acquisition times, high timing accuracy, and high detection sensitivity. Single-photon avalanche diodes (SPADs) have emerged as one of the most promising detector technologies to achieve all of these requirements. However, these detectors are plagued by measurement distortions known as pileup, which fundamentally limit their precision. In this work, we develop a probabilistic image formation model that accurately models pileup. We devise inverse methods to efficiently and robustly estimate scene depth and reflectance from recorded photon counts using the proposed model along with statistical priors. With this algorithm, we not only demonstrate improvements to timing accuracy by more than an order of magnitude compared to the state-of-the-art, but our approach is also the first to facilitate sub-picosecond-accurate, photon-efficient 3D imaging in practical scenarios where widely-varying photon counts are observed.

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Sub-picosecond photon-efficient 3D imaging using single-photon sensors

Abstract

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