74 lines
2.2 KiB
Markdown
74 lines
2.2 KiB
Markdown
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# Why this fork
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This is a fork from the original NeRF++ implementation. The original code has
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some isses I needed to fix in order to get the algorithm working and to be able
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to reproduce the authors work.
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Hopefully this version should be better explained, will the necessary scripts
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and without bugs to enable someone to train a dataset from start to finish
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without having delve into the code if do not want to.
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## How to install
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Create a virtual env if you want to
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```
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pip3 -m venv env
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source env/bin/activate
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```
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Install the needed dependencies
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```
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pip3 install -r requirements.txt
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```
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You will also need [COLMAP](colmap.github.io/)
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## How to run
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First you need to create or find a dataset. A large set of images (at least 30,
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more if you want a 360 degree reconstruction).
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In order to maximise the quality of the reconstuction it is recommended to take
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pictures with the same illumination, from differents angles from the same
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subject (take a step between each picture, do not only rotate).
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Remember that higher quality pictures can always be resized later if needed
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with tools like ImageMagick (mogrify or convert)
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```
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mogrify -resize 800 *.jpg
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```
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Then use the wrapper colmap `colmap_runner/run_colmap.py`
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First change the two lines corresponding to input and output at the end of the
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script `img_dir` is your dataset and `output_dir` is the ouput. If your COLMAP
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binary is not located in `/usr/local/bin/colmap` also change it.
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```
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cd colmap_runner
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python3 run_colmap.py
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```
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Then you will need to use the `format_dataset.py` script to transform the
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wrapper COLMAP binary format data into the datastructure requirements by
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NeRF++.
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You again need to change the `input_path` and `output_path`.
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```
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python3 format_dataset.py
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```
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You then need to create the configuration file, copying the example in
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`configs` and tweaking the values to your need is recommended. Refer to the
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help inside `ddp_train_nerf.py` if you need to understand a parameter.
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```
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python3 ddp_train_nerf.py --config configs/lupo/training_lupo.txt
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```
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Your training should be running, if you want to visualise the training in real
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time using tensorboard you can use:
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```
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tensorboard --logdir logs --host localhost
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```
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And opening the given socket (`ip:port`) in your browser.
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It should be 0.0.0.0:6006
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