Demo Data from EDBT 2026 / reviewers in the wild / expert
Maxx Wu
dblp:379/9895
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
depth estimation |
0.9 | 1 | 2025 | Opportunistic Single-Photon Time of Flight · CVPR 2025 |
Computational photography and imaging
single-photon imaging |
0.9 | 1 | 2025 | Opportunistic Single-Photon Time of Flight · CVPR 2025 |
Computational photography and imaging
time-of-flight imaging |
0.9 | 1 | 2025 | Opportunistic Single-Photon Time of Flight · CVPR 2025 |
Computer vision › 3D vision
3d reconstruction |
0.8 | 1 | 2024 | TurboSL: Dense, Accurate and Fast 3D by Neural Inverse Structured Light · CVPR 2024 |
Computer vision › 3D vision › range sensing
structured light |
0.8 | 1 | 2024 | TurboSL: Dense, Accurate and Fast 3D by Neural Inverse Structured Light · CVPR 2024 |
Physical-layer communications › signal detection
coherent detection |
0.8 | 1 | 2024 | Coherent Optical Modems for Full-Wavefield Lidar · SIGGRAPH Asia 2024 |
Wireless sensing and localization › optical sensing
lidar |
0.8 | 1 | 2024 | Coherent Optical Modems for Full-Wavefield Lidar · SIGGRAPH Asia 2024 |
Physical-layer communications
optical communication |
0.8 | 1 | 2024 | Coherent Optical Modems for Full-Wavefield Lidar · SIGGRAPH Asia 2024 |
Physical-layer communications
signal processing for communications |
0.2 | 1 | 2024 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Opportunistic Single-Photon Time of FlightabstractScattered 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 |
CVPR | 4 |
| 2024 | TurboSL: Dense, Accurate and Fast 3D by Neural Inverse Structured LightabstractWe 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 |
CVPR | 2 |
| 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 Asia | 3 |