Python写的一些小工具
NeRF代码理解
Nerfstudio——简化NeRF流程
| Title | Nerfstudio: A Modular Framework for Neural Radiance Field Development |
|---|---|
| Author | Tancik, Matthew and Weber, Ethan and Ng, Evonne and Li, Ruilong and Yi, Brentand Kerr, Justin and Wang, Terrance and Kristoffersen, Alexander and Austin,Jake and Salahi, Kamyar and Ahuja, Abhik and McAllister, David and Kanazawa,Angjoo |
| Conf/Jour | ACM SIGGRAPH 2023 Conference Proceedings |
| Year | 2023 |
| Project | nerfstudio-project/nerfstudio: A collaboration friendly studio for NeRFs (github.com) |
| Paper | Nerfstudio: A Modular Framework for Neural Radiance Field Development (readpaper.com) |
Nerfstudio提供了一个简单的API，可以简化创建、训练和测试NeRF的端到端过程。该库通过将每个组件模块化，支持更易于理解的NeRF实现。通过更模块化的NeRF，我们希望为探索这项技术提供更用户友好的体验。
Instant-nsr-pl
使用Instant-ngp中的编码技术，使Neus可以更快的进行inference，大概只需要5~10min生成一个模型
NVlabs/instant-ngp: Instant neural graphics primitives: lightning fast NeRF and more (github.com)
zhaofuq/Instant-NSR: Pytorch implementation of fast surface resconstructor (github.com)
kwea123/ngp_pl: Instant-ngp in pytorch+cuda trained with pytorch-lightning (high quality with high speed, with only few lines of legible code) (github.com)
neus：对无纹理的区域处理的很差
NeuS
| Title | NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction |
|---|---|
| Author | Peng Wang Lingjie Liu Yuan Liu Christian Theobalt Taku Komura Wenping Wang |
| Conf/Jour | NeurIPS 2021 (Spotlight) |
| Year | 2021 |
| Project | NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction (lingjie0206.github.io) |
| Paper | NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction (readpaper.com) |
实现了三维重建：从多视角图片中重建出了 mesh 模型
NeRF原理
| Title | NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis |
|---|---|
| Author | Ben Mildenhall*Pratul P. Srinivasan*Matthew Tancik*Jonathan T. BarronRavi RamamoorthiRen Ng |
| Conf/Jour | ECCV 2020 Oral - Best Paper Honorable Mention |
| Year | 2020 |
| Project | NeRF: Neural Radiance Fields (matthewtancik.com) |
| Paper | NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (readpaper.com) |
NeRF（Neural Radiance Fields）是一种用于生成逼真三维场景的计算机图形学方法。通过神经网络对场景中的每个空间点进行建模，NeRF可以估计每个点的颜色和密度信息。利用渲染方程，NeRF能够合成高质量的逼真图像。相较于传统的渲染方法，NeRF能够处理复杂的光照和反射效果，广泛应用于虚拟现实、增强现实、电影制作和游戏开发等领域。然而，NeRF方法仍面临一些挑战，如计算复杂度和对训练数据的依赖性。研究人员正在不断改进NeRF，以提高其效率和扩展性。
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