Science Score: 26.0%
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○CITATION.cff file
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○Scientific vocabulary similarity
Low similarity (4.4%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: GMdigua
- License: mit
- Language: Python
- Default Branch: main
- Size: 44.9 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
NeRFNDC
Instant-NGP recently introduced a Multi-resolution Hash Encoding for neural graphics primitives like NeRFs. The original NVIDIA implementation mainly in C++/CUDA, based on tiny-cuda-nn, can train NeRFs upto 100x faster!
This project is a pure PyTorch implementation of Instant-NGP, built with the purpose of enabling AI Researchers to play around and innovate further upon this method.
This project is built on top of the super-useful NeRF-pytorch implementation.
Instructions
Download the nerf-synthetic dataset from here: Google Drive.
To train a chair HashNeRF model:
python run_nerf.py --config configs/chair.txt --finest_res 512 --log2_hashmap_size 19 --lrate 0.01 --lrate_decay 10
To train for other objects like ficus/hotdog, replace configs/chair.txt with configs/{object}.txt:
Owner
- Login: GMdigua
- Kind: user
- Repositories: 1
- Profile: https://github.com/GMdigua
GitHub Events
Total
- Push event: 4
- Create event: 2
Last Year
- Push event: 4
- Create event: 2
Dependencies
- configargparse *
- imageio >=2.34.0
- kornia >=0.7.1
- numpy >=1.24.4
- opencv-python-headless >=4.9.0.80
- pyvista >=0.43.3
- torch ==2.2.1
- tqdm *