DOKSparse
sparse DOK tensors on GPU, pytorch (by DeMoriarty)
instant-ngp
Instant neural graphics primitives: lightning fast NeRF and more (by NVlabs)
| DOKSparse | instant-ngp | |
|---|---|---|
| 2 | 147 | |
| 2 | 15,388 | |
| - | 1.3% | |
| 4.2 | 6.7 | |
| 10 months ago | 22 days ago | |
| Cuda | Cuda | |
| 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.
DOKSparse
Posts with mentions or reviews of DOKSparse.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-03.
- GDlog: A GPU-Accelerated Deductive Engine
-
tensor.to_sparse() Memory Allocation
If using sparse tensors is a must, you can look into DOK sparse format, which is supported for 2d matrices in scipy. it kinda allows you to access any element of the sparse tensor in constant time, which makes it possible to create your tensor directly in sparse format, skipping the need to create a dense numpy array first. In case you need a GPU version of this, I have a library that implements sparse dok tensor in pytorch and cuda. currently it's GPU only.
instant-ngp
Posts with mentions or reviews of instant-ngp.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-04.
- I want a 3d scanner...
-
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?
-
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.
What are some alternatives?
When comparing DOKSparse and instant-ngp you can also consider the following projects:
cub - [ARCHIVED] Cooperative primitives for CUDA C++. See https://github.com/NVIDIA/cccl
awesome-NeRF - A curated list of awesome neural radiance fields papers
MegBA - MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment
tiny-cuda-nn - Lightning fast C++/CUDA neural network framework
CUDA-Guide - CUDA Guide
nerf-pytorch - A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.
cuhnsw - CUDA implementation of Hierarchical Navigable Small World Graph algorithm
TensoRF - [ECCV 2022] Tensorial Radiance Fields, a novel approach to model and reconstruct radiance fields
TorchPQ - Approximate nearest neighbor search with product quantization on GPU in pytorch and cuda
colmap - COLMAP - Structure-from-Motion and Multi-View Stereo
cccl - CUDA C++ Core Libraries
instant-meshes - Interactive field-aligned mesh generator