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
Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields
Joo Chan Lee, Daniel Rho, Xiangyu Sun, Jong Hwan Ko, Eunbyung Park
PRTGS: Precomputed Radiance Transfer of Gaussian Splats for Real-Time High-Quality Relighting
Yijia Guo, Yuanxi Bai, Liwen Hu, Ziyi Guo, Mianzhi Liu, Yu Cai, Tiejun Huang, Lei Ma
RRM: Relightable assets using Radiance guided Material extraction
Diego Gomez, Julien Philip, Adrien Kaiser, Élie Michel
PanDORA: Casual HDR Radiance Acquisition for Indoor Scenes
Mohammad Reza Karimi Dastjerdi, Frédéric Fortier-Chouinard, Yannick Hold-Geoffroy, Marc Hébert, Claude Demers, Nima Kalantari, Jean-François Lalonde