Projects per year
Abstract / Description of output
In this paper, we present an algorithm for online 3D
reconstruction of dynamic scenes using individual times of arrival
(ToA) of photons recorded by single-photon detector arrays. One
of the main challenges in 3D imaging using single-photon Lidar
is the integration time required to build ToA histograms and
reconstruct reliably 3D profiles in the presence of non-negligible
ambient illumination. This long integration time also prevents the
analysis of rapid dynamic scenes using existing techniques. We
propose a new method which does not rely on the construction
of ToA histograms but allows, for the first time, individual
detection events to be processed online, in a parallel manner in
different pixels, while accounting for the intrinsic spatiotemporal
structure of dynamic scenes. Adopting a Bayesian approach,
a Bayesian model is constructed to capture the dynamics of
the 3D profile and an approximate inference scheme based on
assumed density filtering is proposed, yielding a fast and robust
reconstruction algorithm able to process efficiently thousands
to millions of frames, as usually recorded using single-photon
detectors. The performance of the proposed method, able to
process hundreds of frames per second, is assessed using a series
of experiments conducted with static and dynamic 3D scenes and
the results obtained pave the way to a new family of real-time
3D reconstruction solutions.
reconstruction of dynamic scenes using individual times of arrival
(ToA) of photons recorded by single-photon detector arrays. One
of the main challenges in 3D imaging using single-photon Lidar
is the integration time required to build ToA histograms and
reconstruct reliably 3D profiles in the presence of non-negligible
ambient illumination. This long integration time also prevents the
analysis of rapid dynamic scenes using existing techniques. We
propose a new method which does not rely on the construction
of ToA histograms but allows, for the first time, individual
detection events to be processed online, in a parallel manner in
different pixels, while accounting for the intrinsic spatiotemporal
structure of dynamic scenes. Adopting a Bayesian approach,
a Bayesian model is constructed to capture the dynamics of
the 3D profile and an approximate inference scheme based on
assumed density filtering is proposed, yielding a fast and robust
reconstruction algorithm able to process efficiently thousands
to millions of frames, as usually recorded using single-photon
detectors. The performance of the proposed method, able to
process hundreds of frames per second, is assessed using a series
of experiments conducted with static and dynamic 3D scenes and
the results obtained pave the way to a new family of real-time
3D reconstruction solutions.
Original language | English |
---|---|
Journal | IEEE Transactions on Image Processing |
Volume | 29 |
Early online date | 12 Nov 2019 |
DOIs | |
Publication status | E-pub ahead of print - 12 Nov 2019 |
Fingerprint
Dive into the research topics of 'Fast online 3D reconstruction of dynamic scenes from individual single-photon detection events'. Together they form a unique fingerprint.Projects
- 2 Finished
-
Signal Processing in the Information Age
Davies, M., Hopgood, J., Hospedales, T., Mulgrew, B., Thompson, J., Tsaftaris, S. & Yaghoobi Vaighan, M.
1/07/18 → 31/03/24
Project: Research
-
C-SENSE: Exploiting low dimensional models in sensing, computation and signal processing
1/09/16 → 31/08/22
Project: Research