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20182026
most citedSegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud

18 citations · 35 across the 13 of their papers we have counts for

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9 papers · 1 filter

cs.GR2026

MaterialClusterGS: Palette-Based Material Decomposition and Physically-Based Relighting with 2D Gaussian Splatting

Hao Zhang, Ang Li, Boyan Du +6

We present MaterialClusterGS, a palette-based material decomposition framework for 2D Gaussian Splatting that enables physically based relighting and material editing. Existing Gau…

cs.GR2026

OctaOctree Neural Radiosity for Real-time Glossy Material Rendering

Jierui Ren, Haojie Jin, Bo Pang +4

Modeling high-frequency outgoing radiance distributions remains a fundamental challenge in global illumination, especially for glossy and specular materials. Existing neural-based…

cs.GR2026

PTIR-GS: Path-Traced Inverse Rendering with Global Illumination in 3D Gaussian Fields

Junke Zhu, Hao Zhang, Yutian Zhu +6

Ray tracing enables 3D Gaussian fields to serve as a representation for physically based light transport. Faithful inverse rendering requires forward rendering and backward optimiz…

cs.GR2025

NRRS: Neural Russian Roulette and Splitting

Haojie Jin, Jierui Ren, Yisong Chen +2

We propose a novel framework for Russian Roulette and Splitting (RRS) tailored to wavefront path tracing, a highly parallel rendering architecture that processes path states in bat…

cs.GR2025

Neural Cone Radiosity for Interactive Global Illumination with Glossy Materials

Jierui Ren, Haojie Jin, Bo Pang +3

Modeling of high-frequency outgoing radiance distributions has long been a key challenge in rendering, particularly for glossy material. Such distributions concentrate radiative en…

cs.GR2025

Geometry-Aware Global Feature Aggregation for Real-Time Indirect Illumination

Meng Gai, Guoping Wang, Sheng Li

Real-time rendering with global illumination is crucial to afford the user realistic experience in virtual environments. We present a learning-based estimator to predict diffuse in…