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This repository is currently under development. Features and functionalities may be added or improved in the future.
To set up a conda environment and begin the training process for the lego data, use the following commands:
conda env create -f environment.yml
conda activate tiny_pytorch_nerf
python main.py --npz_file ../data/lego.npz --N_samples 64 --pos_enc 8 --N_iter 1000 --save_pts 100 --width 512 256 128 128 64 --batch_norm False --dropout False
tensorboard --logdir=../logs/lego --port=6006
If everything works without errors, you can now go to localhost:6006 in your browser and watch the "lego" scene train.
usage: main.py [-h] --npz_file NPZ_FILE [--N_samples N_SAMPLES] [--N_iter N_ITER] [--save_pts SAVE_PTS] [--width [WIDTH [WIDTH ...]]]
[--pos_enc POS_ENC] [--batch_norm BATCH_NORM] [--dropout DROPOUT]
Input samples for the training process.
optional arguments:
-h, --help show this help message and exit
--npz_file NPZ_FILE Compressed input numpy file containing: images, poses, and focal info
--N_samples N_SAMPLES
Number of samples in the 3D space (default: 64)
--N_iter N_ITER Number of training iterations (default: 1000)
--save_pts SAVE_PTS Save model every N iterations (default: 100)
--width [WIDTH [WIDTH ...]]
List of model widths (default: [512, 256, 128, 64, 32, 16, 8])
--pos_enc POS_ENC Positional encodings dimension (default: 6)
--batch_norm BATCH_NORM
enable batch normalization (default: False)
--dropout DROPOUT enable dropout (default: False)
The implementation is flexible for users to manipulate by changing the model depth/width, batch normalization, dropout, change the positional encodings, change the dropout and the total number of iteration.
This image shows a subset of the Lego dataset, specifically 100 images that have been resized to 100x100 pixels. These images serve as the input data for training the tiny_pytorch_nerf.
A continuous rendering of a 360-degree view of the Lego dataset after a number of training steps.
We welcome collaborations from the community! If you're interested in contributing to this educational NeRF, please review our contributing guidelines and send us a pull request or open an issue.
Build a more small scale 3D recontruction models for educational perpose. Next we try to replace the NN with ViT and evaluate the result, stay tuned...