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
Distributed Radiance Fields for Edge Video Compression and Metaverse Integration in Autonomous Driving
Eugen Šlapak, Matúš Dopiriak, Mohammad Abdullah Al Faruque, Juraj Gazda, Marco Levorato
Mip-Grid: Anti-aliased Grid Representations for Neural Radiance Fields
Seungtae Nam, Daniel Rho, Jong Hwan Ko, Eunbyung Park