instant-ngp
tiny-cuda-nn
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instant-ngp | tiny-cuda-nn | |
---|---|---|
147 | 9 | |
15,329 | 3,379 | |
2.2% | 3.3% | |
6.7 | 6.3 | |
6 days ago | 23 days ago | |
Cuda | C++ | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
instant-ngp
- I want a 3d scanner...
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Mind-blowing results (LORA/Checkpoint mix)
This is really cool! Could you now use something like this to turn the new images in a 3d model? Or even use open pose (controlnet) to generate a bunch of images from different angles and use InstantNeRF to make a 3d model for free!
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Scanning in real life environments to be viewed in VR >>> taking pictures. Simple process from video -> render, using instant-ngp
It is at this point that you should have Instant-NGP setup. The script for the COLMAP processing is in the repo, as well as the steps to perform it. My exact parameters were 3 fps and 16 aabb. It is pretty helpful to add the scripts directory into path for exact access system-wide.
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[D] NeRF, LeRF, Prolific Dreamer, Neuralangelo, and a lot of other cool NeRF research
[Project Page] https://nvlabs.github.io/instant-ngp/
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Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields
instant-ngp ([1]) from NVIDIA can render NeRF in VR in real-time, assuming a very good desktop video card. Note that instant-ngp is not as photo-realistic as Zip-NeRF. But it's still very good!
1. https://github.com/NVlabs/instant-ngp
- How about Ranger Green?
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Roast my MC kit
Playing around with neRF AI (https://github.com/NVlabs/instant-ngp) to create some 3d gear reveals. I think this a fun way to show off a kit, what do you think?
- Has anyone tried to generate images from enough angles to feed Nvidia Nerf to make 3D models?
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Instant NPG: how do minimize noise and maximize quality? Tips welcome!
3 not sure if it's the one you want but the -aabb_scale is a crop. This page recommends trying a large value of 128 for some outdoor scenes: https://github.com/NVlabs/instant-ngp/blob/master/docs/nerf_dataset_tips.md
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I NeRF'd the new Taco Bell on Rt. 40
I don't know about lumalabs, but basically all NeRF projects these days are based on NVIDIAs Instant neural graphics primitives ( GitHub: instant-ngp). It utilizes COLMAP for SfM (preprocessing step for the neural network) and runs on average Geforce cards pretty good. The fox example (50 photos) on their page literally takes 5 seconds to complete.
tiny-cuda-nn
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[D] Have their been any attempts to create a programming language specifically for machine learning?
In the opposite direction from your question is a very interesting project, TinyNN all implemented as close to the metal as possible and very fast: https://github.com/NVlabs/tiny-cuda-nn
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A CUDA-free instant NGP renderer written entirely in Python: Support real-time rendering and camera interaction and consume less than 1GB of VRAM
This repo only implemented the rendering part of the NGP but is more simple and has a lesser amount of code compared to the original (Instant-NGP and tiny-cuda-nn).
- Tiny CUDA Neural Networks: fast C++/CUDA neural network framework
- Making 3D holograms this weekend with the very “Instant” Neural Graphics Primitives by nvidia — made this volume from 100 photos taken with an old iPhone 7 Plus
- NVlabs/tiny-CUDA-nn: fast C++/CUDA neural network framework
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Small Neural networks in Julia 5x faster than PyTorch
...a C++ library with a CUDA backend. But these high-performance building blocks might only be saturating the GPU fully if the data is large enough.
I haven't looked at implementing these things, but I imagine uf you have smaller networks and thus less data, the large building blocks may not be optimal. You may for example want to fuse some operations to reduce memory latency from repeated memory access.
In PyTorch world, there are approaches for small networks as well, there is https://github.com/NVlabs/tiny-cuda-nn - as far as I understand from the first link in the README, it makes clever use of the CUDA shared memory, which can hold all the weights of a tiny network (but not larger ones).
- [R] Instant Neural Graphics Primitives with a Multiresolution Hash Encoding (Training a NeRF takes 5 seconds!)
- Tiny CUDA Neural Networks
- Real-Time Neural Radiance Caching for Path Tracing
What are some alternatives?
awesome-NeRF - A curated list of awesome neural radiance fields papers
blis - BLAS-like Library Instantiation Software Framework
nerf-pytorch - A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
TensoRF - [ECCV 2022] Tensorial Radiance Fields, a novel approach to model and reconstruct radiance fields
juliaup - Julia installer and version multiplexer
colmap - COLMAP - Structure-from-Motion and Multi-View Stereo
RecursiveFactorization
instant-meshes - Interactive field-aligned mesh generator
RecursiveFactorization.jl
instant-ngp-Windows - Instant neural graphics primitives: lightning fast NeRF and more
vectorflow