VLDB 2026 Research / reviewers in the wild / expert
Xuecan Wang
dblp:374/8655
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › inverse rendering
illumination estimation |
0.8 | 1 | 2024 | LightOctree: Lightweight 3D Spatially-Coherent Indoor Lighting Estimation · CVPR 2024 |
Computer vision › 3D vision › 3d scene modeling
scene representation |
0.8 | 1 | 2024 | LightOctree: Lightweight 3D Spatially-Coherent Indoor Lighting Estimation · CVPR 2024 |
Methods — techniques the papers use, named apart from their topics
volumetric rendering · 0.8differentiable cone tracing · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | LightOctree: Lightweight 3D Spatially-Coherent Indoor Lighting EstimationabstractWe present a lightweight solution for estimating spatially-coherent indoor lighting from a single RGB image. Previous methods for estimating illumination using volumetric repre-sentations have overlooked the sparse distribution of light sources in space, necessitating substantial memory and computational resources for achieving high-quality results. We introduce a unified, voxel octree-based illumination estimation framework to produce 3D spatially-coherent lighting. Additionally, a differentiable voxel octree cone tracing ren-dering layer is proposed to eliminate regular volumetric representation throughout the entire process and ensure the retention of features across different frequency domains. This reduction significantly decreases spatial usage and required floating-point operations without substantially compromising precision. Experimental results demonstrate that our approach achieves high-quality coherent estimation with minimal cost compared to previous methods. Xuecan Wang, Shibang Xiao, Xiaohui Liang 0001 |
CVPR | 1 |