43 citations · 52 across the 13 of their papers we have counts for
14 papers
RRG-SLAM: Real-time Reflection-aware Gaussian SLAM for Indoor Scenes
Yong Liu, Keyang Ye, Zhexi Peng +3
We introduce the first real-time reflection-aware Gaussian SLAM system for indoor scenes. The system features a reflection-aware TSDF-Gaussian hybrid representation that explicitly…
Learning View-Dependent Splatting Kernels
Huakeng Ding, Zhanpeng Liu, Fan Pei +2
We present a differentiable framework to automatically learn view-dependent 2D kernels in a splatting-based pipeline to improve reconstruction quality and representation efficiency…
Depth Peeling for High-Fidelity Gaussian-Enhanced Surfel Rendering
Keyang Ye, Hongzhi Wu, Kun Zhou
Novel view synthesis has been significantly advanced by NeRFs and 3D Gaussian Splatting (3DGS), which require ordering volumetric samples or primitives for correct color blending.…
Differentiable Adaptive 4D Structured Illumination for Joint Capture of Shape and Reflectance
Huakeng Ding, Yaowen Chen, Kun Zhou +1
We present a differentiable framework to adaptively compute 4D illumination conditions with respect to an object, for efficient, high-quality simultaneous acquisition of its shape…
Sparse-to-Complete: From Sparse Image Captures to Complete 3D Scenes
Yiyang Shen, Yin Yang, Kun Zhou +1
We introduce S2C-3D, a novel sparse-view 3D reconstruction framework for high-fidelity and complete scene reconstruction from as few as six to eight images. Our framework features…
Neural Enhancement of Analytical Appearance Models
Xuanzhe Shen, Xiaohe Ma, Kun Zhou +1
Traditional analytical reflectance models, while compact and interpretable, lack the capacity to accurately represent physical measurements. Recent neural models, which closely fit…
TR-Gaussians: High-fidelity Real-time Rendering of Planar Transmission and Reflection with 3D Gaussian Splatting
Yong Liu, Keyang Ye, Tianjia Shao +1
We propose Transmission-Reflection Gaussians (TR-Gaussians), a novel 3D-Gaussian-based representation for high-fidelity rendering of planar transmission and reflection, which are u…
When Gaussian Meets Surfel: Ultra-fast High-fidelity Radiance Field Rendering
Keyang Ye, Tianjia Shao, Kun Zhou
We introduce Gaussian-enhanced Surfels (GESs), a bi-scale representation for radiance field rendering, wherein a set of 2D opaque surfels with view-dependent colors represent the c…
Progressive Radiance Distillation for Inverse Rendering with Gaussian Splatting
Keyang Ye, Qiming Hou, Kun Zhou
We propose progressive radiance distillation, an inverse rendering method that combines physically-based rendering with Gaussian-based radiance field rendering using a distillation…
ElastoGen: 4D Generative Elastodynamics
Yutao Feng, Yintong Shang, Xiang Feng +7
We present ElastoGen, a knowledge-driven AI model that generates physically accurate 4D elastodynamics. Unlike deep models that learn from video- or image-based observations, Elast…
3D Gaussian Splatting with Deferred Reflection
Keyang Ye, Qiming Hou, Kun Zhou
The advent of neural and Gaussian-based radiance field methods have achieved great success in the field of novel view synthesis. However, specular reflection remains non-trivial, a…
Gaussian Splashing: Unified Particles for Versatile Motion Synthesis and Rendering
Yutao Feng, Xiang Feng, Yintong Shang +8
We demonstrate the feasibility of integrating physics-based animations of solids and fluids with 3D Gaussian Splatting (3DGS) to create novel effects in virtual scenes reconstructe…
Animatable 3D Gaussians for High-fidelity Synthesis of Human Motions
Keyang Ye, Tianjia Shao, Kun Zhou
We present a novel animatable 3D Gaussian model for rendering high-fidelity free-view human motions in real time. Compared to existing NeRF-based methods, the model owns better cap…
A Real-time Method for Inserting Virtual Objects into Neural Radiance Fields
Keyang Ye, Hongzhi Wu, Xin Tong +1
We present the first real-time method for inserting a rigid virtual object into a neural radiance field, which produces realistic lighting and shadowing effects, as well as allows…