Pyramid NeRF: Frequency Guided Fast Radiance Field Optimization

Novel view synthesis using implicit neural functions such as Neural Radiance Field (NeRF) has achieved significant progress recently. However, it is very computationally expensive to train a NeRF due to the disordered frequency optimization. In this paper, we propose the Pyramid NeRF, which guides the NeRF training in a `low-frequency first, high-frequency second' style using the image pyramids and could improve the training and inference speed at 15x and 805x, respectively. The high training efficiency is guaranteed by (i) organized frequency-guided optimization could improve the convergency speed and efficiently reduce the training iterations and (ii) progressive subdivision, which replaces a single large multi-layer perceptron (MLP) with thousands of tiny MLPs, could significantly decrease the execution time of running MLPs. Experiments on various synthetic and real scenes verify the high efficiency of the Pyramid NeRF. Meanwhile, the structure and perceptual similarities could be better recovered.