DDNeRF: Depth Distribution Neural Radiance Fields

TL;DR

DDNeRF introduces a novel approach that improves sampling efficiency in neural radiance fields by learning a detailed density distribution along rays, enabling high-quality scene representation with fewer samples and less computation.

Contribution

The paper presents DDNeRF, a method that learns a detailed density distribution to guide sampling, significantly reducing training samples and computational costs while enhancing scene quality.

Findings

Achieves higher quality scene rendering with fewer samples.

Reduces training computational resources.

Outperforms existing NeRF models in efficiency and quality.

Abstract

In recent years, the field of implicit neural representation has progressed significantly. Models such as neural radiance fields (NeRF), which uses relatively small neural networks, can represent high-quality scenes and achieve state-of-the-art results for novel view synthesis. Training these types of networks, however, is still computationally very expensive. We present depth distribution neural radiance field (DDNeRF), a new method that significantly increases sampling efficiency along rays during training while achieving superior results for a given sampling budget. DDNeRF achieves this by learning a more accurate representation of the density distribution along rays. More specifically, we train a coarse model to predict the internal distribution of the transparency of an input volume in addition to the volume's total density. This finer distribution then guides the sampling procedure of the fine model. This method allows us to use fewer samples during training while reducing computational resources.