Radiance Field
Radiance fields are neural representations of 3D scenes that enable novel view synthesis and other advanced capabilities. Current research focuses on improving efficiency and realism, particularly through the use of Gaussian splatting, which offers faster rendering and better handling of view-dependent effects, as well as addressing challenges like handling dynamic scenes, inconsistent lighting conditions, and limited data. These advancements are significant for applications in robotics, virtual and augmented reality, and computer graphics, offering more realistic and efficient 3D scene modeling and manipulation.
Papers
GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling
Bowen Zhang, Yiji Cheng, Jiaolong Yang, Chunyu Wang, Feng Zhao, Yansong Tang, Dong Chen, Baining Guo
Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation
Yujin Chen, Yinyu Nie, Benjamin Ummenhofer, Reiner Birkl, Michael Paulitsch, Matthias Müller, Matthias Nießner
Lifelong LERF: Local 3D Semantic Inventory Monitoring Using FogROS2
Adam Rashid, Chung Min Kim, Justin Kerr, Letian Fu, Kush Hari, Ayah Ahmad, Kaiyuan Chen, Huang Huang, Marcus Gualtieri, Michael Wang, Christian Juette, Nan Tian, Liu Ren, Ken Goldberg
DyBluRF: Dynamic Neural Radiance Fields from Blurry Monocular Video
Huiqiang Sun, Xingyi Li, Liao Shen, Xinyi Ye, Ke Xian, Zhiguo Cao