  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.
