The conventional mesh-based Level of Detail (LoD) technique, exemplified by
applications such as Google Earth and many game engines, exhibits the
capability to holistically represent a large scene even the Earth, and achieves
rendering with a space complexity of O(log n). This constrained data
requirement not only enhances rendering efficiency but also facilitates dynamic
data fetching, thereby enabling a seamless 3D navigation experience for users.
In this work, we extend this proven LoD technique to Neural Radiance Fields
(NeRF) by introducing an octree structure to represent the scenes in different
scales. This innovative approach provides a mathematically simple and elegant
representation with a rendering space complexity of O(log n), aligned with the
efficiency of mesh-based LoD techniques. We also present a novel training
strategy that maintains a complexity of O(n). This strategy allows for parallel
training with minimal overhead, ensuring the scalability and efficiency of our
proposed method. Our contribution is not only in extending the capabilities of
existing techniques but also in establishing a foundation for scalable and
efficient large-scale scene representation using NeRF and octree structures.

通过引入八叉树结构以不同比例表示场景，将传统的基于网格的细节层次技术扩展到神经辐射场，提供了数学简洁、优雅的表示，同时保持渲染空间复杂度为 O (log n)。同时，我们提出了一种新的训练策略，保证了方法的可扩展性和效率，既扩展了现有技术的能力，又为使用 NeRF 和八叉树结构进行可扩展、高效的大规模场景表示奠定了基础。

InfNeRF：具有 O (log n) 空间复杂度的无限规模 NeRF 渲染

InfNeRF: Towards Infinite Scale NeRF Rendering with O(log n) Space  Complexity

Remarkable progress has been made in 4D content generation recently. However,
existing methods suffer from long optimization time, lack of motion
controllability, and a low level of detail. In this paper, we introduce
DreamGaussian4D, an efficient 4D generation framework that builds on 4D
Gaussian Splatting representation. Our key insight is that the explicit
modeling of spatial transformations in Gaussian Splatting makes it more
suitable for the 4D generation setting compared with implicit representations.
DreamGaussian4D reduces the optimization time from several hours to just a few
minutes, allows flexible control of the generated 3D motion, and produces
animated meshes that can be efficiently rendered in 3D engines.

DreamGaussian4D 是一种高效的 4D 生成框架，它建立在 4D 高斯喷洒表示的基础上，通过显式地建模高斯喷洒中的空间变换，提高了 4D 生成的适用性，同时减少了优化时间，增加了对生成的三维动画的灵活控制，产生的动画网格在 3D 引擎中能被高效渲染。

DreamGaussian4D: 生成 4D 高斯飞溅

DreamGaussian4D: Generative 4D Gaussian Splatting

Neural signed distance functions (SDFs) are emerging as an effective
representation for 3D shapes. State-of-the-art methods typically encode the SDF
with a large, fixed-size neural network to approximate complex shapes with
implicit surfaces. Rendering with these large networks is, however,
computationally expensive since it requires many forward passes through the
network for every pixel, making these representations impractical for real-time
graphics. We introduce an efficient neural representation that, for the first
time, enables real-time rendering of high-fidelity neural SDFs, while achieving
state-of-the-art geometry reconstruction quality. We represent implicit
surfaces using an octree-based feature volume which adaptively fits shapes with
multiple discrete levels of detail (LODs), and enables continuous LOD with SDF
interpolation. We further develop an efficient algorithm to directly render our
novel neural SDF representation in real-time by querying only the necessary
LODs with sparse octree traversal. We show that our representation is 2-3
orders of magnitude more efficient in terms of rendering speed compared to
previous works. Furthermore, it produces state-of-the-art reconstruction
quality for complex shapes under both 3D geometric and 2D image-space metrics.

本篇研究提出了一种基于八叉树的特征体积的神经无人机距离函数模型，实现了高保真的 3D 形状实时渲染，且在多个方面表现出全球领先的复杂形状重建质量与渲染效率。