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Yi-Ruei Liu

dblp:391/2969 · DBLP profile ↗
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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 · 62% Geometric modeling and processing · 38%

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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
3d reconstruction
0.912025
SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes · CVPR 2025
Computer vision › 3D vision › 3d scene reconstruction
dynamic scene reconstruction
0.912025
SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes · CVPR 2025
Rendering
neural rendering
0.912025
SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes · CVPR 2025
Geometric modeling and processing › surface processing
surface normal estimation
0.312025
SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes · CVPR 2025
Geometric modeling and processing
surface reconstruction
0.312025
SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes · CVPR 2025

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

physically-based rendering · 1.7deformation field · 1.73d gaussian splatting · 1.7
YearPublicationVenuePosition
2025 SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes
abstract
We present SpectroMotion, a novel approach that combines 3D Gaussian Splatting (3DGS) with physically-based rendering (PBR) and deformation fields to reconstruct dynamic specular scenes. Previous methods extending 3DGS to model dynamic scenes have struggled to represent specular surfaces accurately. Our method addresses this limitation by introducing a residual correction technique for accurate surface normal computation during deformation, complemented by a deformable environment map that adapts to time-varying lighting conditions. We implement a coarse-to-fine training strategy significantly enhancing scene geometry and specular color prediction. It is the only existing 3DGS method capable of synthesizing photorealistic real-world dynamic specular scenes, outperforming state-of-the-art methods in rendering complex, dynamic, and specular scenes. Please see our project page at cdfan0627.github.io/spectromotion.
Cheng-De Fan, Chen-Wei Chang, Yi-Ruei Liu, Jie-Ying Lee, Jiun-Long Huang, Yu-Chee Tseng, Yu-Lun Liu 0001
CVPR3