awesome-NeRF
WaveFunctionCollapse
awesome-NeRF | WaveFunctionCollapse | |
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18 | 99 | |
6,250 | 22,745 | |
0.9% | - | |
6.9 | 4.8 | |
1 day ago | 5 days ago | |
TeX | C# | |
MIT License | GNU General Public License v3.0 or later |
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awesome-NeRF
- 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
WaveFunctionCollapse
- I use Wave Function Collapse to create levels for my game (2022) [video]
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It's Okay to Make Something Nobody Wants
Thank you! And yes, I agree. I was looking at uh https://github.com/mxgmn/WaveFunctionCollapse and wondering if that were applicable here :)
Have a good day!
- The Wavefunction Collapse Algorithm
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Kullback–Leibler Divergence
Intuitively, it measures the difference between two probability distributions. It's not symmetric, so it's not quite that, but in my opinion, it's good intuition.
As motivation, say you're an internet provider, providing internet service to a business. You naturally want to save money, so you perhaps want to compress packets before they go over the wire. Let's say the business you're providing service to also compresses their data, but they've made a mistake and do it inefficiently.
Let's say the business has, incorrectly, determined the probability distribution for their data to be $q(x)$. That is, they assign probability of seeing symbol $x$ to be $q(x)$. Let's say you've determined the "true" distribution to be $p(x)$. The entropy, or number of bits, they expect to transmit per packet/symbol will be $-\sum p(x) lg(q(x))$. Meaning, they'll compress their stream under the assumption that the distribution is $q(x)$ but the actually probability of seeing a packet, $x$, is $p(x)$, which is why the term $p(x) lg(q(x))$ shows up.
The number of bits you're transmitting is just $-\sum p(x) lg(p(x))$. Now we ask, how many bits, per packet, is the savings of your method over the businesses? This is $-\sum p(x) lg(q(x)/p(x))$, which is exactly the Kullback-Leibler divergence (maybe up to a sign difference).
In other words, given a "guess" at a distribution and the "true" distribution, how bad is it between them? This is the Kullback-Leibler distribution and why it shows up (I believe) in machine learning and fitness functions.
As a more concrete example, I just ran across a paper talking [0] about using WFC [1] to asses how well it, and other algorithms, do when trying to create generative "super mario brothers" like levels. Take a 2x2 or 3x3 grid, make a library of tiles, use that to generate a random level, then use the K-L divergence to determine how well your generative algorithm has done compared to the observed distribution from an example image.
[0] https://arxiv.org/pdf/1905.05077.pdf
[1] https://github.com/mxgmn/WaveFunctionCollapse
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All of it under the most poorly designed and maintained village
Reminds me of wave function collapse - a programmatic way to generate mazes.
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How to detect and fix isolated terrain (islands or lakes) in a tile-based terrain?
I am using WFC to generate the terrain, with pretty much a copy-paste implementation of the original WFC implemented into Unity.
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How to make wfc or post-gen script in blender?
If you still want to go the WFC route, the original WFC repository is a great place to start. There's also a (relatively barebones looking) Godot plugin you could take a look at.
- Wave Function Collapse
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collapsed
wave function collapse studies - this is done with the https://github.com/mxgmn/WaveFunctionCollapse algorithm after I saw https://github.com/CodingTrain/Wave-Function-Collapse mention it. done in P5! IG https://www.instagram.com/ronivonu/
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Room Generation Using Constraint Satisfaction
There’s an interesting approach similar to this called [Wave Function Collapse](https://github.com/mxgmn/WaveFunctionCollapse) (no relation to wfc in physics idea besides inspiration). It can infer the probabilistic constraints from one input example, and it seems to generalize quite well. Here’s a [little demo](https://oskarstalberg.com/game/wave/wave.html)
What are some alternatives?
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
Raylib-cs - C# bindings for raylib, a simple and easy-to-use library to learn videogames programming
colmap - COLMAP - Structure-from-Motion and Multi-View Stereo
eShopOnContainers - Cross-platform .NET sample microservices and container based application that runs on Linux Windows and macOS. Powered by .NET 7, Docker Containers and Azure Kubernetes Services. Supports Visual Studio, VS for Mac and CLI based environments with Docker CLI, dotnet CLI, VS Code or any other code editor. Moved to https://github.com/dotnet/eShop.
nerf - Code release for NeRF (Neural Radiance Fields)
OpenFK - An open source replacement for the U.B. Funkeys executable.
gaugan - Photorealistic landscape drawings using the Nvidia SPADE model
DeBroglie - DeBroglie is a C# library implementing the Wave Function Collapse algorithm with support for additional non-local constraints, and other useful features.
svox2 - Plenoxels: Radiance Fields without Neural Networks
dnSpy-Unity-mono - Fork of Unity mono that's used to compile mono.dll with debugging support enabled
awesome-visual-slam - :books: The list of vision-based SLAM / Visual Odometry open source, blogs, and papers
texture-synthesis - 🎨 Example-based texture synthesis written in Rust 🦀