Image-based 3D reconstruction is a challenging task that involves inferring
the 3D shape of an object or scene from a set of input images. Learning-based
methods have gained attention for their ability to directly estimate 3D shapes.
This review paper focuses on state-of-the-art techniques for 3D reconstruction,
including the generation of novel, unseen views. An overview of recent
developments in the Gaussian Splatting method is provided, covering input
types, model structures, output representations, and training strategies.
Unresolved challenges and future directions are also discussed. Given the rapid
progress in this domain and the numerous opportunities for enhancing 3D
reconstruction methods, a comprehensive examination of algorithms appears
essential. Consequently, this study offers a thorough overview of the latest
advancements in Gaussian Splatting.

图像基于的三维重建是一项具有挑战性的任务，涉及从一组输入图像中推断出对象或场景的三维形状。基于学习的方法因其直接估计三维形状的能力而受到关注。本综述论文着重介绍了三维重建的最新技术，包括生成新颖未见视角的方法。论文提供了对高斯激波方法的最新发展的概述，包括输入类型、模型结构、输出表示和训练策略。未解决的挑战和未来的研究方向也得到了讨论。鉴于该领域的快速进展以及增强三维重建方法的众多机会，对算法进行全面的研究至关重要。因此，本研究全面概述了高斯激波最新进展。

高斯散角法：3D 重建与新景象合成综述

Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review

Neural Radiance Fields (NeRF) have recently emerged as a powerful method for
image-based 3D reconstruction, but the lengthy per-scene optimization limits
their practical usage, especially in resource-constrained settings. Existing
approaches solve this issue by reducing the number of input views and
regularizing the learned volumetric representation with either complex losses
or additional inputs from other modalities. In this paper, we present KeyNeRF,
a simple yet effective method for training NeRF in few-shot scenarios by
focusing on key informative rays. Such rays are first selected at camera level
by a view selection algorithm that promotes baseline diversity while
guaranteeing scene coverage, then at pixel level by sampling from a probability
distribution based on local image entropy. Our approach performs favorably
against state-of-the-art methods, while requiring minimal changes to existing
NeRF codebases.

KeyNeRF 是一种在少样本情况下训练 NeRF 的简单而有效的方法，通过关注关键信息光线，首先通过一个视图选择算法在相机级别选择这样的光线，该算法在促进基线多样性的同时保证场景全面覆盖，然后通过基于局部图像熵的概率分布进行像素级的采样。我们的方法在与最先进的方法进行比较时表现出色，同时对现有的 NeRF 代码库的改动要求较小。