The 3D Gaussian splatting method has drawn a lot of attention, thanks to its
high performance in training and high quality of the rendered image. However,
it uses anisotropic Gaussian kernels to represent the scene. Although such
anisotropic kernels have advantages in representing the geometry, they lead to
difficulties in terms of computation, such as splitting or merging two kernels.
In this paper, we propose to use isotropic Gaussian kernels to avoid such
difficulties in the computation, leading to a higher performance method. The
experiments confirm that the proposed method is about {\bf 100X} faster without
losing the geometry representation accuracy. The proposed method can be applied
in a large range applications where the radiance field is needed, such as 3D
reconstruction, view synthesis, and dynamic object modeling.

本文提出了一种使用各向同性高斯核避免计算困难的高效方法，实验证实该方法在保持几何表示精度的同时大约快了 100 倍，并可应用于需要辐射场的广泛应用领域，如 3D 重建、视图合成和动态物体建模。

实时辐射场渲染的各向同性高斯散落

Isotropic Gaussian Splatting for Real-Time Radiance Field Rendering

Kernel estimation is generally one of the key problems for blind image
super-resolution (SR). Recently, Double-DIP proposes to model the kernel via a
network architecture prior, while KernelGAN employs the deep linear network and
several regularization losses to constrain the kernel space. However, they fail
to fully exploit the general SR kernel assumption that anisotropic Gaussian
kernels are sufficient for image SR. To address this issue, this paper proposes
a normalizing flow-based kernel prior (FKP) for kernel modeling. By learning an
invertible mapping between the anisotropic Gaussian kernel distribution and a
tractable latent distribution, FKP can be easily used to replace the kernel
modeling modules of Double-DIP and KernelGAN. Specifically, FKP optimizes the
kernel in the latent space rather than the network parameter space, which
allows it to generate reasonable kernel initialization, traverse the learned
kernel manifold and improve the optimization stability. Extensive experiments
on synthetic and real-world images demonstrate that the proposed FKP can
significantly improve the kernel estimation accuracy with less parameters,
runtime and memory usage, leading to state-of-the-art blind SR results.

本文提出了一种基于正交流的核先验（FKP），可以有效地解决缺失约束的核估计问题，其中，FKP 可以在隐空间中优化核而不是网络参数空间，从而提供了合理的核初始化，遍历学习到的核流形并提高优化稳定性。