Neural Radiance Fields (NeRF) with hybrid representations have shown
impressive capabilities in reconstructing scenes for view synthesis, delivering
high efficiency. Nonetheless, their performance significantly drops with sparse
view inputs, due to the issue of overfitting. While various regularization
strategies have been devised to address these challenges, they often depend on
inefficient assumptions or are not compatible with hybrid models. There is a
clear need for a method that maintains efficiency and improves resilience to
sparse views within a hybrid framework. In this paper, we introduce an accurate
and efficient few-shot neural rendering method named Spatial Annealing
smoothing regularized NeRF (SANeRF), which is specifically designed for a
pre-filtering-driven hybrid representation architecture. We implement an
exponential reduction of the sample space size from an initially large value.
This methodology is crucial for stabilizing the early stages of the training
phase and significantly contributes to the enhancement of the subsequent
process of detail refinement. Our extensive experiments reveal that, by adding
merely one line of code, SANeRF delivers superior rendering quality and much
faster reconstruction speed compared to current few-shot NeRF methods. Notably,
SANeRF outperforms FreeNeRF by 0.3 dB in PSNR on the Blender dataset, while
achieving 700x faster reconstruction speed.

提出了一种名为 SANeRF 的准确高效的小样本神经渲染方法，能够在混合表示架构中提供更好的渲染质量和更快的重建速度。

高效少样本神经渲染的空间退火平滑化

Spatial Annealing Smoothing for Efficient Few-shot Neural Rendering

In many image-related tasks, learning expressive and discriminative
representations of images is essential, and deep learning has been studied for
automating the learning of such representations. Some user-centric tasks, such
as image recommendations, call for effective representations of not only images
but also preferences and intents of users over images. Such representations are
termed \emph{hybrid} and addressed via a deep learning approach in this paper.
We design a dual-net deep network, in which the two sub-networks map input
images and preferences of users into a same latent semantic space, and then the
distances between images and users in the latent space are calculated to make
decisions. We further propose a comparative deep learning (CDL) method to train
the deep network, using a pair of images compared against one user to learn the
pattern of their relative distances. The CDL embraces much more training data
than naive deep learning, and thus achieves superior performance than the
latter, with no cost of increasing network complexity. Experimental results
with real-world data sets for image recommendations have shown the proposed
dual-net network and CDL greatly outperform other state-of-the-art image
recommendation solutions.

本篇文章提出了一种双网络深度学习方法，将图像和用户偏好映射到同一潜在语义空间，并在该空间中计算图像和用户之间的距离以做出决策，从而解决了用户偏好需要纳入考虑的图像推荐问题；同时，通过比较深度学习的方式利用图像对训练数据实现了优化，提高了推荐准确性。