EDBT 2026 Demo / reviewers in the wild / expert
Alan Fu
dblp:405/3657
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
depth estimation |
0.9 | 1 | 2025 | Focal Split: Untethered Snapshot Depth from Differential Defocus · CVPR 2025 |
Computational photography and imaging › depth estimation
depth from defocus |
0.9 | 1 | 2025 | Focal Split: Untethered Snapshot Depth from Differential Defocus · CVPR 2025 |
Computational photography and imaging
depth imaging |
0.9 | 1 | 2025 | Focal Split: Untethered Snapshot Depth from Differential Defocus · CVPR 2025 |
Methods — techniques the papers use, named apart from their topics
differential defocus · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Focal Split: Untethered Snapshot Depth from Differential DefocusabstractWe introduce Focal Split, a handheld, snapshot depth camera with fully onboard power and computing based on depth-from-differential-defocus (DfDD). Focal Split is passive, avoiding power consumption of light sources. Its achromatic optical system simultaneously forms two differentially defocused images of the scene, which can be independently captured using two photosensors in a snapshot. The data processing is based on the DfDD theory, which efficiently computes a depth and a confidence value for each pixel with only 500 floating point operations (FLOPs) per pixel from the camera measurements. We demonstrate a Focal Split prototype, which comprises a handheld custom camera system connected to a Raspberry Pi 5 for real-time data processing. The system consumes 4.9 W and is powered on a 5 V, 10,000 mAh battery. The prototype can measure objects with distances from 0.4 m to 1.2 m, outputting 480×360 sparse depth maps at 2.1 frames per second (FPS) using unoptimized Python scripts. Focal Split is DIY friendly. A comprehensive guide to building your own Focal Split depth camera, code, and additional data can be found at https://focal-split.qiguo.org. Junjie Luo 0009, John Mamish, Alan Fu, Thomas Concannon, Josiah D. Hester, Emma Alexander, Qi Guo 0009 |
CVPR | 3 |