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
performance. Experiments on two public datasets demonstrate that SfMNeRF
surpasses state-of-the-art approaches. Code is available at
this https URL

SfMNeRF 提出了一种基于神经辐射场的方法，结合自监督深度估计方法对样本进行重构，同时利用极线和光度一致性等约束构建 3D 场景几何图像，提高了神经辐射场的表现，实现了更好地合成新视角。