CVPR 2023, highlight paper - F2-NeRF: Fast Neural Radiance Field Training with Free Camera Trajectories

In this episode we discuss F2-NeRF: Fast Neural Radiance Field Training with Free Camera Trajectories by Peng Wang, Yuan Liu, Zhaoxi Chen, Lingjie Liu, Ziwei Liu, Taku Komura, Christian Theobalt, Wenping Wang. The paper presents a new grid-based NeRF called F2-NeRF which allows arbitrary input camera trajectories and is faster to train. Existing fast grid-based NeRF training frameworks are designed for bounded scenes and rely on space warping but cannot process arbitrary trajectories. The paper proposes a new space-warping method called perspective warping to handle unbounded scenes and demonstrates its effectiveness through experiments on standard and newly collected datasets.