EDBT 2026 Demo / reviewers in the wild / expert
Numfor Mbiziwo-Tiapo
dblp:405/5337
· 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.
| Artificial intelligence
1 paper |
3D vision · 100% | |
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 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
3d scene reconstruction |
0.9 | 1 | 2025 | DRAWER: Digital Reconstruction and Articulation With Environment Realism · CVPR 2025 |
Computer vision › 3D vision › 3d reconstruction › object reconstruction
articulated object reconstruction |
0.9 | 1 | 2025 | DRAWER: Digital Reconstruction and Articulation With Environment Realism · CVPR 2025 |
Virtual and augmented reality › virtual environment
interactive virtual environments |
0.9 | 1 | 2025 | DRAWER: Digital Reconstruction and Articulation With Environment Realism · CVPR 2025 |
Methods — techniques the papers use, named apart from their topics
real-to-sim transfer · 1.7dual scene representation · 1.7articulation estimation · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | DRAWER: Digital Reconstruction and Articulation With Environment RealismabstractCreating virtual digital replicas from real-world data unlocks significant potential across domains like gaming and robotics. In this paper, we present DRAWER, a novel framework that converts a video of a static indoor scene into a photorealistic and interactive digital environment. Our approach centers on two main contributions: (i) a reconstruction module based on a dual scene representation that reconstructs the scene with fine-grained geometric details, and (ii) an articulation module that identifies articulation types and hinge positions, reconstructs simulatable shapes and appearances and integrates them into the scene. The resulting virtual environment is photorealistic, interactive, and runs in real time, with compatibility for game engines and robotic simulation platforms. We demonstrate the potential of DRAWER by using it to automatically create an interactive game in Unreal Engine and to enable real-to-sim-to-real transfer for robotics applications. Project page: here. Hongchi Xia, Entong Su, Marius Memmel, Arhan Jain, Raymond Yu, Numfor Mbiziwo-Tiapo, Ali Farhadi, Abhishek Gupta 0004, Shenlong Wang, Wei-Chiu Ma |
CVPR | 6 |