Novel view synthesis has recently made significant progress with the advent
of Neural Radiance Fields (NeRF). DietNeRF is an extension of NeRF that aims to
achieve this task from only a few images by introducing a new loss function for
unknown viewpoints with no input images. The loss function assumes that a
pre-trained feature extractor should output the same feature even if input
images are captured at different viewpoints since the images contain the same
object. However, while that assumption is ideal, in reality, it is known that
as viewpoints continuously change, also feature vectors continuously change.
Thus, the assumption can harm training. To avoid this harmful training, we
propose ManifoldNeRF, a method for supervising feature vectors at unknown
viewpoints using interpolated features from neighboring known viewpoints. Since
the method provides appropriate supervision for each unknown viewpoint by the
interpolated features, the volume representation is learned better than
DietNeRF. Experimental results show that the proposed method performs better
than others in a complex scene. We also experimented with several subsets of
viewpoints from a set of viewpoints and identified an effective set of
viewpoints for real environments. This provided a basic policy of viewpoint
patterns for real-world application. The code is available at
this https URL

ManifoldNeRF 是继 DietNeRF 后的一种新方法，通过使用来自相邻已知视点的插值特征对未知视点的特征向量进行监督，从而提供了更好的体积表示学习，实验证明该方法比其他方法在复杂场景中表现更好。

ManifoldNeRF: 面向视角相关的图像特征监督的少样本神经辐射场

ManifoldNeRF: View-dependent Image Feature Supervision for Few-shot  Neural Radiance Fields

We present DietNeRF, a 3D neural scene representation estimated from a few
images. Neural Radiance Fields (NeRF) learn a continuous volumetric
representation of a scene through multi-view consistency, and can be rendered
from novel viewpoints by ray casting. While NeRF has an impressive ability to
reconstruct geometry and fine details given many images, up to 100 for
challenging 360{\deg} scenes, it often finds a degenerate solution to its image
reconstruction objective when only a few input views are available. To improve
few-shot quality, we propose DietNeRF. We introduce an auxiliary semantic
consistency loss that encourages realistic renderings at novel poses. DietNeRF
is trained on individual scenes to (1) correctly render given input views from
the same pose, and (2) match high-level semantic attributes across different,
random poses. Our semantic loss allows us to supervise DietNeRF from arbitrary
poses. We extract these semantics using a pre-trained visual encoder such as
CLIP, a Vision Transformer trained on hundreds of millions of diverse
single-view, 2D photographs mined from the web with natural language
supervision. In experiments, DietNeRF improves the perceptual quality of
few-shot view synthesis when learned from scratch, can render novel views with
as few as one observed image when pre-trained on a multi-view dataset, and
produces plausible completions of completely unobserved regions.

DietNeRF 通过引入辅助语义一致性损失，使用预训练的视觉编码器，改进了使用较少图像时的神经辐射场的视图合成，从而提高了感知质量。