EDBT 2026 Demo / reviewers in the wild / expert
Yuehu Gong
dblp:401/7961
· 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 |
Rendering · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › 3d reconstruction › surface reconstruction
gaussian splatting surface reconstruction |
0.9 | 1 | 2025 | MGSR: 2D/3D Mutual-boosted Gaussian Splatting for High-fidelity Surface Reconstruction Under Various Light Conditions · ICCV 2025 |
Computer vision › 3D vision › 3d reconstruction
surface reconstruction |
0.9 | 1 | 2025 | MGSR: 2D/3D Mutual-boosted Gaussian Splatting for High-fidelity Surface Reconstruction Under Various Light Conditions · ICCV 2025 |
Rendering › neural rendering
gaussian splatting rendering |
0.9 | 1 | 2025 | MGSR: 2D/3D Mutual-boosted Gaussian Splatting for High-fidelity Surface Reconstruction Under Various Light Conditions · ICCV 2025 |
Rendering
novel view synthesis |
0.9 | 1 | 2025 | MGSR: 2D/3D Mutual-boosted Gaussian Splatting for High-fidelity Surface Reconstruction Under Various Light Conditions · ICCV 2025 |
Methods — techniques the papers use, named apart from their topics
mutual supervision · 1.73d gaussian splatting · 1.72d gaussian splatting · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | MGSR: 2D/3D Mutual-boosted Gaussian Splatting for High-fidelity Surface Reconstruction Under Various Light ConditionsabstractNovel view synthesis (NVS) and surface reconstruction (SR) are essential tasks in 3D Gaussian Splatting (3D-GS). Despite recent progress, these tasks are often addressed independently, with GS-based rendering methods struggling under diverse light conditions and failing to produce accurate surfaces, while GS-based reconstruction methods frequently compromise rendering quality. This raises a central question: must rendering and reconstruction always involve a trade-off? To address this, we propose MGSR, a 2D/3D Mutual-boosted Gaussian splatting for Surface Reconstruction that enhances both rendering quality and 3D reconstruction accuracy. MGSR introduces two branches--one based on 2D-GS and the other on 3D-GS. The 2D-GS branch excels in surface reconstruction, providing precise geometry information to the 3D-GS branch. Leveraging this geometry, the 3D-GS branch employs a geometry-guided illumination decomposition module that captures reflected and transmitted components, enabling realistic rendering under varied light conditions. Using the transmitted component as supervision, the 2D-GS branch also achieves high-fidelity surface reconstruction. Throughout the optimization process, the 2D-GS and 3D-GS branches undergo alternating optimization, providing mutual supervision. Prior to this, each branch completes an independent warm-up phase, with an early stopping strategy implemented to reduce computational costs. We evaluate MGSR on a diverse set of synthetic and real-world datasets, at both object and scene levels, demonstrating strong performance in rendering and surface reconstruction. Code is available at https://github.com/TsingyuanChou/MGSR. Qingyuan Zhou, Yuehu Gong, Weidong Yang 0001, Yeqi Luo, Baixin Xu, Shuhao Li 0001, Ben Fei, Ying He 0001 |
ICCV | 2 |