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Maxx Wu

dblp:379/9895 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2025
0009-0006-4161-3374ORCID · corroborated

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

Graphics, computer vision, multimedia, augmented reality and games · 3 · 3 since 2021Artificial intelligence and machine learning · 2 · 2 since 2021Human-computer interaction and ubiquitous computing · 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 networks
1 paper
Physical-layer communications · 70% Wireless sensing and localization · 30%
Artificial intelligence
2 papers
3D vision · 100%
Computer graphics and multimedia
1 paper
Computational photography and imaging · 100%

Topics — the 9 heaviest of 9, 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
Computer vision › 3D vision
3d reconstruction
0.812024
TurboSL: Dense, Accurate and Fast 3D by Neural Inverse Structured Light · CVPR 2024
Computer vision › 3D vision › range sensing
structured light
0.812024
TurboSL: Dense, Accurate and Fast 3D by Neural Inverse Structured Light · CVPR 2024
Physical-layer communications › signal detection
coherent detection
0.812024
Coherent Optical Modems for Full-Wavefield Lidar · SIGGRAPH Asia 2024
Wireless sensing and localization › optical sensing
lidar
0.812024
Coherent Optical Modems for Full-Wavefield Lidar · SIGGRAPH Asia 2024
Physical-layer communications
optical communication
0.812024
Coherent Optical Modems for Full-Wavefield Lidar · SIGGRAPH Asia 2024
Physical-layer communications
signal processing for communications
0.212024
Coherent Optical Modems for Full-Wavefield Lidar · SIGGRAPH Asia 2024

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

frequency-domain analysis · 1.7SPAD camera · 1.7signed distance field · 0.8optical modem design · 0.8neural inverse rendering · 0.8
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
CVPR4
2024 TurboSL: Dense, Accurate and Fast 3D by Neural Inverse Structured Light
abstract
We show how to turn a noisy and fragile active triangulation technique-three-pattern structured light with a grayscale camera-into a fast and powerful tool for 3D capture: able to output sub-pixel accurate disparities at megapixel resolution, along with reflectance, normals, and a no-reference estimate of its own pixelwise 3D error. To achieve this, we formulate structured-light decoding as a neural inverse rendering problem. We show that despite having just three or four input images-all from the same viewpoint-this problem can be tractably solved by TurboSL, an algorithm that combines (1) a precise image formation model, (2) a signed distance field scene representation, and (3) projection Pattern sequences optimized for accuracy instead of precision. We use TurboSL to reconstruct a variety of complex scenes from images captured at up to 60 fps with a camera and a common projector. Our experiments highlight TurboSL's potential for dense and highly-accurate 3D acquisition from data captured in fractions of a second.
Parsa Mirdehghan, Maxx Wu, Wenzheng Chen, David B. Lindell, Kiriakos N. Kutulakos
CVPR2
2024 Coherent Optical Modems for Full-Wavefield Lidar
Parsa Mirdehghan, Brandon Buscaino, Maxx Wu, Doug Charlton, Mohammad E. Mousa-Pasandi, Kiriakos N. Kutulakos, David B. Lindell
SIGGRAPH Asia3