Sketched RT3D: How to Reconstruct Billions of Photons Per Second

Julián Tachella, Mikey Sheehan, Michael E. Davies

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Single-photon light detection and ranging (lidar) captures depth and intensity information of a 3D scene. Reconstructing a scene from observed photons is a challenging task due to spurious detections associated with background illumination sources. To tackle this problem, there is a plethora of 3D reconstruction algorithms which exploit spatial regularity of natural scenes to provide stable reconstructions. However, most existing algorithms have computational and memory complexity proportional to the number of recorded photons. This complexity hinders their real-time deployment on modern lidar arrays which acquire billions of photons per second. Leveraging a recent lidar sketching framework, we show that it is possible to modify existing reconstruction algorithms such that they only require a small sketch of the photon information. In particular, we propose a sketched version of a recent state-of-the-art algorithm which uses point cloud denoisers to provide spatially regularized reconstructions. A series of experiments performed on real lidar datasets demonstrates a significant reduction of execution time and memory requirements, while achieving the same reconstruction performance than in the full data case.
Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE Xplore
Pages1566-1570
Number of pages5
ISBN (Electronic)978-1-6654-0540-9
ISBN (Print)978-1-6654-0541-6
DOIs
Publication statusE-pub ahead of print - 27 Apr 2022
EventICASSP 2022: IEEE International Conference on Acoustics, Speech and Signal Processing - Singapore
Duration: 7 May 2022 → …
https://2022.ieeeicassp.org

Publication series

NameInternational Conference on Acoustics, Speech, and Signal Processing (ICASSP)
PublisherIEEE
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

ConferenceICASSP 2022
Abbreviated titleICASSP 2022
Period7/05/22 → …
Internet address

Keywords / Materials (for Non-textual outputs)

  • 3D reconstruction
  • Inverse problems
  • compressive learning
  • single-photon lidar

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