April 2, 2024, 7:48 p.m. | Yang Liu, He Guan, Chuanchen Luo, Lue Fan, Junran Peng, Zhaoxiang Zhang

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.01133v1 Announce Type: new
Abstract: The advancement of real-time 3D scene reconstruction and novel view synthesis has been significantly propelled by 3D Gaussian Splatting (3DGS). However, effectively training large-scale 3DGS and rendering it in real-time across various scales remains challenging. This paper introduces CityGaussian (CityGS), which employs a novel divide-and-conquer training approach and Level-of-Detail (LoD) strategy for efficient large-scale 3DGS training and rendering. Specifically, the global scene prior and adaptive training data selection enables efficient training and seamless fusion. Based …

arxiv cs.cv quality real-time rendering scale type

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Data Scientist

@ Publicis Groupe | New York City, United States

Bigdata Cloud Developer - Spark - Assistant Manager

@ State Street | Hyderabad, India