20 citations · 22 across the 7 of their papers we have counts for
8 papers
UGOD: Uncertainty-Guided Opacity and Dropout for Sparse-View 3D Gaussian Splatting
Zhihao Guo, Peng Wang, Zidong Chen +7
Sparse-view 3D Gaussian Splatting is prone to overfitting because limited observations leave many Gaussian primitives weakly constrained, yet their contributions are still accumula…
Semantic-Aware, Physics-Informed, Geometry-Grounded Weather Video Synthesis
Chenghao Qian, Nedko Savov, Lingdong Kong +7
Weather synthesis aims to add weather effects to input videos while preserving scene identity, structure, and motion. The key limitation of existing methods is the lack of diversit…
Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception
Yuhu Guo, Zhikai Shen, Jiasheng Qu +4
Tactile perception is key to dexterous manipulation, yet simulating high-resolution elastomer deformation remains computationally prohibitive. Finite element methods (FEM) deliver…
LoViF 2026 The First Challenge on Weather Removal in Videos
Chenghao Qian, Xin Li, Yeying Jin +23
This paper presents a review of the LoViF 2026 Challenge on Weather Removal in Videos. The challenge encourages the development of methods for restoring clean videos from inputs de…
WeatherEdit: Controllable Weather Editing with 4D Gaussian Field
Chenghao Qian, Wenjing Li, Yuhu Guo +1
In this work, we present WeatherEdit, a novel weather editing pipeline for generating realistic weather effects with controllable types and severity in 3D scenes. Our approach is s…
WeatherGS: 3D Scene Reconstruction in Adverse Weather Conditions via Gaussian Splatting
Chenghao Qian, Yuhu Guo, Wenjing Li +1
3D Gaussian Splatting (3DGS) has gained significant attention for 3D scene reconstruction, but still suffers from complex outdoor environments, especially under adverse weather. Th…
WeatherDG: LLM-assisted Diffusion Model for Procedural Weather Generation in Domain-Generalized Semantic Segmentation
Chenghao Qian, Yuhu Guo, Yuhong Mo +1
In this work, we propose a novel approach, namely WeatherDG, that can generate realistic, weather-diverse, and driving-screen images based on the cooperation of two foundation mode…
AllWeatherNet:Unified Image Enhancement for Autonomous Driving under Adverse Weather and Lowlight-conditions
Chenghao Qian, Mahdi Rezaei, Saeed Anwar +4
Adverse conditions like snow, rain, nighttime, and fog, pose challenges for autonomous driving perception systems. Existing methods have limited effectiveness in improving essentia…