Mip-NeRF RGB-D: Depth Assisted Fast Neural Radiance Fields

TL;DR

This paper enhances Neural Radiance Fields by integrating depth information and modeling depth uncertainty, leading to more accurate scene geometry, fewer artifacts, and significantly faster training and inference times.

Contribution

It introduces a depth-augmented Mip-NeRF that models depth uncertainty, improving geometry accuracy and reducing training and prediction times.

Findings

Improved scene geometry accuracy

Reduced artifacts in reconstructions

Training time decreased by 3-5 times

Abstract

Neural scene representations, such as Neural Radiance Fields (NeRF), are based on training a multilayer perceptron (MLP) using a set of color images with known poses. An increasing number of devices now produce RGB-D(color + depth) information, which has been shown to be very important for a wide range of tasks. Therefore, the aim of this paper is to investigate what improvements can be made to these promising implicit representations by incorporating depth information with the color images. In particular, the recently proposed Mip-NeRF approach, which uses conical frustums instead of rays for volume rendering, allows one to account for the varying area of a pixel with distance from the camera center. The proposed method additionally models depth uncertainty. This allows to address major limitations of NeRF-based approaches including improving the accuracy of geometry, reduced artifacts, faster training time, and shortened prediction time. Experiments are performed on well-known benchmark scenes, and comparisons show improved accuracy in scene geometry and photometric reconstruction, while reducing the training time by 3 - 5 times.