Warm-start NeRF: Accelerating Per-scene Training of NeRF-based Light-Field Representation

Takuto Nishio, Chihiro Tsutake, Keita Takahashi, Toshiaki Fujii

Abstract: A light field is represented as a set of multi-view images captured from a dense 2-D array of viewpoints. To treat a light field as being continuous, we represent it as a neural radiance field (NeRF), which is a learned representation of a 3-D scene. NeRFs are renowned for their ability to reconstruct a target 3-D scene with compelling visual quality, but they are slow to train. A solution for this problem is to use a tiny neural network and trainable volumetric features as the scene representation, which is considered the baseline of our research. For further acceleration, we propose a method for warm-starting the per-scene training by setting good initial values for the trainable parameters. To this end, we introduce another encoder network to obtain the initial volumetric features from the target light field. Starting with the appropriate initial values, our method can achieve better rendering quality with fewer training iterations than the baseline.

External IDs:dblp:conf/vcip/NishioTTF24