Xuecan Wang

dblp:374/8655 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › inverse rendering
illumination estimation
0.812024
LightOctree: Lightweight 3D Spatially-Coherent Indoor Lighting Estimation · CVPR 2024
Computer vision › 3D vision › 3d scene modeling
scene representation
0.812024
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
YearPublicationVenuePosition
2024 LightOctree: Lightweight 3D Spatially-Coherent Indoor Lighting Estimation
abstract
We 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
CVPR1