awesome-NeRF
A curated list of awesome neural radiance fields papers (by awesome-NeRF)
colmap
COLMAP - Structure-from-Motion and Multi-View Stereo (by colmap)
| awesome-NeRF | colmap | |
|---|---|---|
| 18 | 28 | |
| 6,250 | 6,794 | |
| 0.9% | 2.7% | |
| 6.9 | 9.2 | |
| 1 day ago | 3 days ago | |
| TeX | C++ | |
| MIT License | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-NeRF
Posts with mentions or reviews of awesome-NeRF.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-10-05.
- Recommendation for a convenient NERF model to try out? And discussions...
- This is not drone footage or an iPhone video but An AI model made this. Google researchers created this 3D scene and walkthrough using just 2D images. This is called a NeRF (Anti-Aliased Grid-Based Neural Radiance Fields), where AI models can take 2D pictures and create 3D scenes.
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New AI Tools for iPhones: Motion Capture and Environment Scanning - But What About Android Users?
And plenty others from outside Google Research. However, I wasn't aware there was a whole product making the creation of them trivial already. It's great to see honestly as I was hoping this would make the leap from research to products given how useful it is.
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Is Nerf better than COLMAP for object reconstruction?
You can extract a mesh from a NeRF by using the marching cubes algorithm. But you'll have to texture it as well, and only after that can you be real time. NeRF training or inference isn't fast in the vanilla version. I suggest you browse https://github.com/awesome-NeRF/awesome-NeRF and look for the fast versions, this one is very fast, if you're ok with special CUDA kernels: https://nvlabs.github.io/instant-ngp/
- I volunteered to help out with the Awesome NeRF list - help me bring it up to date.
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Nerf meshes are crap and thats normal right ?
There are implementations of NeRF that are solely based on the aim of a good export, and the work is going fast. I'm mobile right now but think this is a good place to watch, if my bookmarks are correct: https://github.com/yenchenlin/awesome-NeRF
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Nvidia NeRF
https://github.com/yenchenlin/awesome-NeRF watch and learn from this page!
- A curated list of NeRF papers & other resources
colmap
Posts with mentions or reviews of colmap.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-08-03.
- Magic123: One Image to High-Quality 3D Object Generation
-
Drone mapping is pretty dang cool
Not saying its easy to use, but there is an application gui and it is free: https://github.com/colmap/colmap
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Import many photogrammetry software's scenes into Blender
Colmap (Model folders (BIN and TXT), dense workspaces, NVM, PLY)
- Best options for monocular reconstruction?
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improving camera pose estimation using multiple aruco markers
See colmap for example https://colmap.github.io/
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2D images to 3D Object reconstruction
You're looking into a problem called photogrammetry, and a well-studied one at that. I'd recommend looking into "shape from motion" (sfm); specifically techniques that do "dense reconstruction." I'd recommend COLMAP to start with. It does pose estimation from images (e.g. you point it at a bunch of images and it will figure out the relative poses of the cameras that took them), as well as sparse and dense reconstcution.
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Framework generate 3d meshes from camera images
COLMAP builds dense meshes from a collection of cameras https://colmap.github.io/
- Nerfstudio: A collaboration friendly studio for NeRFs
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Neural Radiance Fields and input shape
I’ve seen references to using COLMAP (https://colmap.github.io/) to estimate camera position/pose, e.g. here
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3D reconstruction of an object from videos/few images
Classical photogrammetry, where I agree with u/tdgros that the way to go is https://colmap.github.io/. There are actually better variants in literature but nothing is more reliable and user-friendly than COLMAP. This will give you a very precise point cloud, that can be meshed if needed.
What are some alternatives?
When comparing awesome-NeRF and colmap you can also consider the following projects:
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
Meshroom - 3D Reconstruction Software
nerf - Code release for NeRF (Neural Radiance Fields)
OpenMVG (open Multiple View Geometry) - open Multiple View Geometry library. Basis for 3D computer vision and Structure from Motion.
gaugan - Photorealistic landscape drawings using the Nvidia SPADE model
Hierarchical-Localization - Visual localization made easy with hloc
svox2 - Plenoxels: Radiance Fields without Neural Networks
awesome-visual-slam - :books: The list of vision-based SLAM / Visual Odometry open source, blogs, and papers
openMVS - open Multi-View Stereo reconstruction library
sketch-to-art - 🖼 Create artwork from your casual sketch with GAN and style transfer
awesome-NeRF vs instant-ngp
colmap vs Meshroom
awesome-NeRF vs nerf
colmap vs OpenMVG (open Multiple View Geometry)
awesome-NeRF vs gaugan
colmap vs Hierarchical-Localization
awesome-NeRF vs svox2
colmap vs nerf
awesome-NeRF vs awesome-visual-slam
colmap vs openMVS
awesome-NeRF vs sketch-to-art
colmap vs instant-ngp