Image-Based Reconstruction
A 3D reconstruction pipeline making use of both implicit and explicit surface representations
Installation
Only Linux is supported (Tested on Ubuntu 22.04)
Run the following command to create a conda envrionment
conda env create -f environment.yml
Then please follow the instructions in https://pytorch.org/ to install a recent version of PyTorch with cuda support (tested on PyTorch 1.11 w/ cuda 11.7)
After installing PyTorch, please install Tiny Cuda Neural Network by running the following command
pip install -r requirements.txt
Then run build_psdr.sh to build enoki and psdr-cuda after installing the required dependencies listed in the official doc of psdr
source build_psdr.sh /path/to/optix/sdk /path/to/python/include/dir
Finally please follow the instructions in large-steps-pytorch to install the submodule
Run the download_dataset_nerf_synthetic.py script to download the nerf synthetic dataset and put the nerf_synthetic folder under the ./data directory. Run the preprocess_nerf_synthetic() function in util.py before optimizing
After each stage of optmization you will have to run the postprocess.py to post-process the optimization results in order to do run the next stage or to get the optimized mesh. This is due to some subtle bug that makes it difficult to dump the results directly after optimization.
Please run setpath.sh to add some necessary envrionment variables before the first run
source setpath.sh
Run optimize_dmtet.py to run the first stage of optimization. The results are in output/scene_name/ where scene_name is the name field in the config file.
Run optimize_mesh.py to run the second stage after running the first stage and post-processing the results. The results of the second stage are in output/scene_name/ where scene_name is the name field in the config file.