Divide and Conquer: Rethinking the Training Paradigm of Neural Radiance
  Fields
Rongkai Ma Leo Lebrat Rodrigo Santa Cruz Gil Avraham Yan Zuo Clinton Fookes Olivier Salvado
Neural radiance fields (NeRFs) have exhibited potential in synthesizing
high-fidelity views of 3D scenes but the standard training paradigm of NeRF
presupposes an equal importance for each image in the training set. This
assumption poses a significant challenge for rendering specific views
presenting intricate geometries, thereby resulting in suboptimal performance.
In this paper, we take a closer look at the implications of the current
training paradigm and redesign this for more superior rendering quality by
NeRFs. Dividing input views into multiple groups based on their visual
similarities and training individual models on each of these groups enables
each model to specialize on specific regions without sacrificing speed or
efficiency. Subsequently, the knowledge of these specialized models is
aggregated into a single entity via a teacher-student distillation paradigm,
enabling spatial efficiency for online render-ing. Empirically, we evaluate our
novel training framework on two publicly available datasets, namely NeRF
synthetic and Tanks&Temples. Our evaluation demonstrates that our DaC training
pipeline enhances the rendering quality of a state-of-the-art baseline model
while exhibiting convergence to a superior minimum.