activity
20182026
most citedRevisiting Dimensionality Reduction Techniques for Visual Cluster Analysis: An Empirical Study

84 citations · 102 across the 9 of their papers we have counts for

collaborators

12 papers

cs.CV2026

G-Skin: Learning to Bind 3D Gaussians with Generative Visual Priors

Yuxin Yao, Kendong Liu, Shiqi Zhou +2

3D Gaussian Splatting has achieved remarkable success in photorealistic and efficient rendering, leading to a rapid increase in 3D assets represented by 3D Gaussian primitives. Dir…

cs.CV2026

SPARE-GS: Structural Parsimony and Resource Efficiency for 3D Gaussian Splatting

Zhang Chen, Shuai Wan, Fuzheng Yang +3

3D Gaussian Splatting (3DGS) achieves high-fidelity novel view synthesis in real-time; however its training efficiency and representation compactness are hindered by excessive prim…

cs.CV2025

FlexPara: Flexible Neural Surface Parameterization

Yuming Zhao, Qijian Zhang, Junhui Hou +3

Surface parameterization is a fundamental geometry processing task, laying the foundations for the visual presentation of 3D assets and numerous downstream shape analysis scenarios…

cs.CV2024

LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation

Chenxu Zhou, Lvchang Fu, Sida Peng +5

This paper targets the challenge of real-time LiDAR re-simulation in dynamic driving scenarios. Recent approaches utilize neural radiance fields combined with the physical modeling…

cs.HC202184 cited

Revisiting Dimensionality Reduction Techniques for Visual Cluster Analysis: An Empirical Study

Jiazhi Xia, Yuchen Zhang, Jie Song +3

Dimensionality Reduction (DR) techniques can generate 2D projections and enable visual exploration of cluster structures of high-dimensional datasets. However, different DR techniq…

cs.SI2020

Preserving Minority Structures in Graph Sampling

Ying Zhao, Haojin Jiang, Qi'an Chen +6

Sampling is a widely used graph reduction technique to accelerate graph computations and simplify graph visualizations. By comprehensively analyzing the literature on graph samplin…