instruct-nerf2nerf
nerfstudio
| instruct-nerf2nerf | nerfstudio | |
|---|---|---|
| 2 | 10 | |
| 752 | 8,605 | |
| - | 3.5% | |
| 7.2 | 9.6 | |
| 3 months ago | 6 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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instruct-nerf2nerf
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GTA 6 trailer screencaps, photorealistic style
There is already text editing NeRFs, too https://instruct-nerf2nerf.github.io/
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ReconFusion: 3D Reconstruction from just a few photos using a Diffusion Prior
The other one that really impressed me was using text instructions (similar to SD) with NeRFs to edit NeRFs https://github.com/ayaanzhaque/instruct-nerf2nerf
nerfstudio
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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
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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
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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
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NeRF ➜ point cloud export — now available via nerfstudio
nerf.studio | github | discord
- Show HN: A collaboration friendly studio for NeRFs
What are some alternatives?
text2cinemagraph - Text2Cinemagraph: Text-Guided Synthesis of Eulerian Cinemagraphs [SIGGRAPH ASIA 2023]
multinerf - A Code Release for Mip-NeRF 360, Ref-NeRF, and RawNeRF
TorchSharp - A .NET library that provides access to the library that powers PyTorch.
sdfstudio - A Unified Framework for Surface Reconstruction
smerf-3d
vision_transformer
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).
CIPS-3D - 3D-aware GANs based on NeRF (arXiv).
taichi-nerfs - Implementations of NeRF variants based on Taichi + PyTorch
SegmentAnythingin3D - Segment Anything in 3D with NeRFs (NeurIPS 2023)
beartype - Unbearably fast near-real-time hybrid runtime-static type-checking in pure Python.
3d-transforms - 3D Transforms is a library to easily work with 3D data and make 3D transformations. This library originally started as a few functions here and there for my own work which I then turned into a library.