David Barmherzig

dblp:211/7202 · also David A. Barmherzig · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0003-2466-981XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Computational photography and imaging · 100%
Artificial intelligence
1 paper
3D vision · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
depth estimation
0.912025
Opportunistic Single-Photon Time of Flight · CVPR 2025
Computational photography and imaging
single-photon imaging
0.912025
Opportunistic Single-Photon Time of Flight · CVPR 2025
Computational photography and imaging
time-of-flight imaging
0.912025
Opportunistic Single-Photon Time of Flight · CVPR 2025

Methods — techniques the papers use, named apart from their topics

frequency-domain analysis · 1.7SPAD camera · 1.7
YearPublicationVenuePosition
2025 Opportunistic Single-Photon Time of Flight
abstract
Scattered light from pulsed lasers is increasingly part of our ambient illumination, as many devices rely on them for active 3D sensing. In this work, we ask: can these “ambient” light signals be detected and leveraged for passive 3D vision? We show that pulsed lasers, despite being weak and fluctuating at MHz to GHz frequencies, leave a distinctive sinc comb pattern in the temporal frequency domain of incident flux that is specific to each laser and invariant to the scene. This enables their passive detection and analysis with a free-running SPAD camera, even when they are unknown, asynchronous, out of sight, and emitting concurrently. We show how to synchronize with such lasers computationally, characterize their pulse emissions, separate their contributions, and—if many are present—localize them in 3D and recover a depth map of the camera’s field of view. We use our camera prototype to demonstrate (1) a first-of-its-kind visualization of asynchronously propagating light pulses from multiple lasers through the same scene, (2) passive estimation of a laser’s MHz-scale pulse repetition frequency with mHz precision, and (3) mm-scale 3D imaging over room-scale distances by passively harvesting photons from two or more out-of-view lasers.
Sotiris Nousias, Mian Wei, Howard Xiao, Maxx Wu, Shahmeer Athar, Kevin J. Wang, Anagh Malik, David Barmherzig, David B. Lindell, Kiriakos N. Kutulakos
CVPR8
2021 Recovering Missing Data in Coherent Diffraction Imaging
abstract
A method is presented for inferring missing low-frequency data in coherent diffraction imaging via the least-squares solution of a linear system. This gives rise to improved image reconstruction when solving the phase retrieval problem.
David Barmherzig, Alex H. Barnett, Charles L. Epstein, Leslie Greengard, Jeremy F. Magland, Manas Rachh
SIAM J. Imaging Sci.1