Single-Photon Depth Imaging Using a Union-of-Subspaces Model
Light detection and ranging systems reconstruct scene depth from time-of-flight measurements. For low light-level depth imaging applications, such as remote sensing and robot vision, these systems use single-photon detectors that resolve individual photon arrivals. Even so, they must detect a large...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2016
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| Online Access: | http://hdl.handle.net/1721.1/101046 https://orcid.org/0000-0001-9289-829X https://orcid.org/0000-0002-6094-5861 |
| _version_ | 1826200992158318592 |
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| author | Shin, Dongeek Shapiro, Jeffrey H. Goyal, Vivek K |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Shin, Dongeek Shapiro, Jeffrey H. Goyal, Vivek K |
| author_sort | Shin, Dongeek |
| collection | MIT |
| description | Light detection and ranging systems reconstruct scene depth from time-of-flight measurements. For low light-level depth imaging applications, such as remote sensing and robot vision, these systems use single-photon detectors that resolve individual photon arrivals. Even so, they must detect a large number of photons to mitigate Poisson shot noise and reject anomalous photon detections from background light. We introduce a novel framework for accurate depth imaging using a small number of detected photons in the presence of an unknown amount of background light that may vary spatially. It employs a Poisson observation model for the photon detections plus a union-of-subspaces constraint on the discrete-time flux from the scene at any single pixel. Together, they enable a greedy signal-pursuit algorithm to rapidly and simultaneously converge on accurate estimates of scene depth and background flux, without any assumptions on spatial correlations of the depth or background flux. Using experimental single-photon data, we demonstrate that our proposed framework recovers depth features with 1.7 cm absolute error, using 15 photons per image pixel and an illumination pulse with 6.7-cm scaled root-mean-square length. We also show that our framework outperforms the conventional pixelwise log-matched filtering, which is a computationally-efficient approximation to the maximum-likelihood solution, by a factor of 6.1 in absolute depth error. |
| first_indexed | 2024-09-23T11:44:57Z |
| format | Article |
| id | mit-1721.1/101046 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:44:57Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1010462022-09-27T21:38:53Z Single-Photon Depth Imaging Using a Union-of-Subspaces Model Shin, Dongeek Shapiro, Jeffrey H. Goyal, Vivek K Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Shin, Dongeek Shapiro, Jeffrey H. Light detection and ranging systems reconstruct scene depth from time-of-flight measurements. For low light-level depth imaging applications, such as remote sensing and robot vision, these systems use single-photon detectors that resolve individual photon arrivals. Even so, they must detect a large number of photons to mitigate Poisson shot noise and reject anomalous photon detections from background light. We introduce a novel framework for accurate depth imaging using a small number of detected photons in the presence of an unknown amount of background light that may vary spatially. It employs a Poisson observation model for the photon detections plus a union-of-subspaces constraint on the discrete-time flux from the scene at any single pixel. Together, they enable a greedy signal-pursuit algorithm to rapidly and simultaneously converge on accurate estimates of scene depth and background flux, without any assumptions on spatial correlations of the depth or background flux. Using experimental single-photon data, we demonstrate that our proposed framework recovers depth features with 1.7 cm absolute error, using 15 photons per image pixel and an illumination pulse with 6.7-cm scaled root-mean-square length. We also show that our framework outperforms the conventional pixelwise log-matched filtering, which is a computationally-efficient approximation to the maximum-likelihood solution, by a factor of 6.1 in absolute depth error. Samsung (Firm) (Scholarship) National Science Foundation (U.S.) (Grant 1422034) Lincoln Laboratory. Advanced Concepts Committee 2016-02-02T00:36:15Z 2016-02-02T00:36:15Z 2015-09 2015-08 Article http://purl.org/eprint/type/JournalArticle 1070-9908 1558-2361 http://hdl.handle.net/1721.1/101046 Shin, Dongeek, Jeffrey H. Shapiro, and Vivek K Goyal. “Single-Photon Depth Imaging Using a Union-of-Subspaces Model.” IEEE Signal Process. Lett. 22, no. 12 (December 2015): 2254–2258. https://orcid.org/0000-0001-9289-829X https://orcid.org/0000-0002-6094-5861 en_US http://dx.doi.org/10.1109/lsp.2015.2475274 IEEE Signal Processing Letters Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Shin, Dongeek Shapiro, Jeffrey H. Goyal, Vivek K Single-Photon Depth Imaging Using a Union-of-Subspaces Model |
| title | Single-Photon Depth Imaging Using a Union-of-Subspaces Model |
| title_full | Single-Photon Depth Imaging Using a Union-of-Subspaces Model |
| title_fullStr | Single-Photon Depth Imaging Using a Union-of-Subspaces Model |
| title_full_unstemmed | Single-Photon Depth Imaging Using a Union-of-Subspaces Model |
| title_short | Single-Photon Depth Imaging Using a Union-of-Subspaces Model |
| title_sort | single photon depth imaging using a union of subspaces model |
| url | http://hdl.handle.net/1721.1/101046 https://orcid.org/0000-0001-9289-829X https://orcid.org/0000-0002-6094-5861 |
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