nerfstudio
beartype
nerfstudio | beartype | |
---|---|---|
10 | 18 | |
8,533 | 2,430 | |
2.7% | 2.8% | |
9.6 | 9.4 | |
3 days ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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.
nerfstudio
-
Smerf: Streamable Memory Efficient Radiance Fields
You’re under the right paper for doing this. Instead of one big model, they have several smaller ones for regions in the scene. This way rendering is fast for large scenes.
This is similar to Block-NeRF [0], in their project page they show some videos of what you’re asking.
As for an easy way of doing this, nothing out-of-the-box. You can keep an eye on nerfstudio [1], and if you feel brave you could implement this paper and make a PR!
[0] https://waymo.com/intl/es/research/block-nerf/
[1] https://github.com/nerfstudio-project/nerfstudio
- Researchers create open-source platform for Neural Radiance Field development
-
first attempt to photogrammetry using DJI mini 2 and metashape. 460 images manual. What did I do wrong? What can i do to improve it? Would appreciate all kinds of advice to a newbie
Try rendering NERFs with your footage, you're gonna love the result and NERFs are pretty robust to reflections. You can use your Metashape solve for Nerf Studio https://github.com/nerfstudio-project/nerfstudio
-
What is the best way to create a dataset for NeRF?
Beyond these tips, I don't have much. There's lots of research about how to improve quality of solves in the software itself. I'm hoping these get added to instant-ngp, since it's fast and free, but it is research software, not a product, so we'll see. Another thing to maybe look at is Nerfstudio. It can use instant-ngp as a solver, but there are other solvers. I briefly tried it but couldn't figure out how it worked, from the small bit of time I spent with it. I hope to get back to it.
- Nerfstudio – A collaboration friendly studio for NeRFs
- When the client's management is happy but their dev team is a pain
- A collaboration friendly studio for NeRFs
-
NeRF ➜ point cloud export — now available via nerfstudio
nerf.studio | github | discord
- Show HN: A collaboration friendly studio for NeRFs
beartype
-
Writing Python Like Rust
https://github.com/beartype/beartype
I wish more people started using Beartype, it makes Python bearable
-
ChatGPT Git Hook Writes Your Commit Messages
I saw this on /r/Python the other day...
- When the client's management is happy but their dev team is a pain
-
Returning to snake's nest after a long journey, any major advances in python for science ?
As other folks have commented, type hints are now a big deal. For static typing the best checker is pyright. For runtime checking there is typeguard and beartype. These can be integrated with array libraries through jaxtyping. (Which also works for PyTorch/numpy/etc., despite the name.)
-
What are some features you wish Python had?
Maybe you're looking for https://github.com/beartype/beartype for runtime type enforcement; it's only at function calls, though, but probably a decent solution for codebases that are not completely typed for MyPy or pyright.
-
svg.py: Type-safe and powerful Python library to generate SVG files
It is though, if you add a type checker to your pipeline and use it without any escape hatches such as `Any` or `type: ignore`, you are essentially making the promise that your code is statically typed. But I say it is a matter of perspective because in my opinion runtime type checking should be avoided if we can get away with statically typed code, but there are type checkers that perform runtime type checking via annotations such as [Beartype](https://github.com/beartype/beartype) (with some trickery like assuming homogenous data structures as to not have to check every element of every structure). Anyway the definition of "type safe" is not 100% even in compiled languages.
- Python’s “Type Hints” are a bit of a disappointment to me
-
What's the best practice to validate parameter types at runtime in Python, with and without a third-party module?
There is the beartype project.
-
Statically typed Python
Personally I find working around mypy's quirks to be more effort than it's worth, so to offer another option: typeguard or beartype can be used to perform run-time type checking.
- Beartype: Unbearably fast runtime type checking in Python
What are some alternatives?
multinerf - A Code Release for Mip-NeRF 360, Ref-NeRF, and RawNeRF
typeguard - Run-time type checker for Python
TorchSharp - A .NET library that provides access to the library that powers PyTorch.
pydantic - Data validation using Python type hints
sdfstudio - A Unified Framework for Surface Reconstruction
mypy - Optional static typing for Python
smerf-3d
mypyc - Compile type annotated Python to fast C extensions
vision_transformer
toit - Program your microcontrollers in a fast and robust high-level language.
kaolin-wisp - NVIDIA Kaolin Wisp is a PyTorch library powered by NVIDIA Kaolin Core to work with neural fields (including NeRFs, NGLOD, instant-ngp and VQAD).
benchmarks - Some benchmarks of different languages