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Chenyanzhen

dblp:396/8690 · DBLP profile ↗
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1ranked-venue papers
0as 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 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
Rendering · 93% Computational photography and imaging · 7%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Rendering › appearance acquisition › material acquisition
BRDF estimation
0.812024
RobIR: Robust Inverse Rendering for High-Illumination Scenes · NeurIPS 2024
Rendering
inverse rendering
0.812024
RobIR: Robust Inverse Rendering for High-Illumination Scenes · NeurIPS 2024
Rendering › inverse rendering
neural inverse rendering
0.812024
RobIR: Robust Inverse Rendering for High-Illumination Scenes · NeurIPS 2024
Rendering
physically based rendering
0.812024
RobIR: Robust Inverse Rendering for High-Illumination Scenes · NeurIPS 2024
Computational photography and imaging
illumination modeling
0.212024
RobIR: Robust Inverse Rendering for High-Illumination Scenes · NeurIPS 2024

Methods — techniques the papers use, named apart from their topics

regularized visibility estimation · 0.8implicit neural representation · 0.8ACES tone mapping · 0.8
YearPublicationVenuePosition
2024 RobIR: Robust Inverse Rendering for High-Illumination Scenes
abstract
Implicit representation has opened up new possibilities for inverse rendering. However, existing implicit neural inverse rendering methods struggle to handle strongly illuminated scenes with significant shadows and slight reflections. The existence of shadows and reflections can lead to an inaccurate understanding of the scene, making precise factorization difficult. To this end, we present RobIR, an implicit inverse rendering approach that uses ACES tone mapping and regularized visibility estimation to reconstruct accurate BRDF of the object. By accurately modeling the indirect radiance field, normal, visibility, and direct light simultaneously, we are able to accurately decouple environment lighting and the object's PBR materials without imposing strict constraints on the scene. Even in high-illumination scenes with shadows and specular reflections, our method can recover high-quality albedo and roughness with no shadow interference. RobIR outperforms existing methods in both quantitative and qualitative evaluations.
Ziyi Yang 0008, Chenyanzhen, Yazhen Yuan, Xiaogang Jin 0001
NeurIPS2