Can I read Improving Neural Radiance Fields with Depth-aware Optimization for Novel View Synthesis on EtoBox?

Improving Neural Radiance Fields with Depth-aware Optimization for Novel View Synthesis by Chen, Shu; Li, Junyao; Zhang, Yang; Zou, Beiji is a scholarly article available to read on EtoBox.

What is Improving Neural Radiance Fields with Depth-aware Optimization for Novel View Synthesis about?

With dense inputs, Neural Radiance Fields (NeRF) is able to render photo-realistic novel views under static conditions. Although the synthesis quality is excellent, existing NeRF-based methods fail to obtain moderate three-dimensional (3D) structures. The novel view synthesis quality drops dramatically given sparse input due to the implicitly reconstructed inaccurate 3D-scene structure. We propose SfMNeRF, a method to better synthesize novel views as well as reconstruct the 3D-scene geometry. SfMNeRF leverages the knowledge from the self-supervised depth estimation methods to constrain the 3D-scene geometry during view synthesis training. Specifically, SfMNeRF employs the epipolar, photometric consistency, depth smoothness, and position-of-matches constraints to explicitly reconstruct the 3D-scene structure. Through these explicit constraints and the implicit constraint from NeRF, our method improves the view synthesis as well as the 3D-scene geometry performance of NeRF at the same time. In addition, SfMNeRF synthesizes novel sub-pixels in which the ground truth is obtained by image interpolation. This strategy enables SfMNeRF to include more samples to improve generalization perf

Author

Chen, Shu; Li, Junyao; Zhang, Yang; Zou, Beiji

Published

2023

Language

EN