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
Real-Time Radiance Fields for Single-Image Portrait View Synthesis
Alex Trevithick, Matthew Chan, Michael Stengel, Eric R. Chan, Chao Liu, Zhiding Yu, Sameh Khamis, Manmohan Chandraker, Ravi Ramamoorthi, Koki Nagano
Inverse Global Illumination using a Neural Radiometric Prior
Saeed Hadadan, Geng Lin, Jan Novák, Fabrice Rousselle, Matthias Zwicker
Neural Image-based Avatars: Generalizable Radiance Fields for Human Avatar Modeling
Youngjoong Kwon, Dahun Kim, Duygu Ceylan, Henry Fuchs
Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos
Liao Wang, Qiang Hu, Qihan He, Ziyu Wang, Jingyi Yu, Tinne Tuytelaars, Lan Xu, Minye Wu
Evaluate Geometry of Radiance Fields with Low-frequency Color Prior
Qihang Fang, Yafei Song, Keqiang Li, Li Shen, Huaiyu Wu, Gang Xiong, Liefeng Bo