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My NeRF implementation with code for blender and omniverse code dataset generation. STATUS: main branch works with blender datasets, I recommend using blendernerf to generate poses and images. Still doing some cleaning to make it simpler to use.

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abubake/bakernerf

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Howdy!

To get started, the requirements file contains everything that must be installed after cloning the repo. Simply run the following command to create it, we recommend installing within a conda environment you create:

Then, create a conda environment and install the requirements within it.

conda env create myEnviroName
conda activate myEnviroName
pip install -r requirements.txt

How to use this program:

The program main functionalities are in the jupyter .ipynb files:

  • processing_training_data - Use this file to process training data you generated from blender or omniverse code, so that it can be used in Nerf-reconstruction
  • Nerf-reconstruction - Program to train a NeRF given rays (can be used for unzipped data you already have)
  • Testing - Use this to test PSNR in rendering novel views
  • MeshExtraction - Use this to generate a 3D mesh from your trained model
  • visualize - Use this to visualize pose and ray data for one or many poses

To run with an existing dataset, simply unzip it in the datasets folder.

Results:

image

image

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My NeRF implementation with code for blender and omniverse code dataset generation. STATUS: main branch works with blender datasets, I recommend using blendernerf to generate poses and images. Still doing some cleaning to make it simpler to use.

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