Pretrained models at checkpoint 50 for ODGnet and checkpoint 43 for fssdf is here https://drive.google.com/drive/folders/1f-DPbcvOMFSfN61VTeiJiA_br67tZA6w?usp=sharing

1. Vamshi kumar Reddy - Pointnet.py model add data_augment function line 186-208, all CUDE environment changes probably 35-55 lines, overall around 50 lines.

Setup the pipeline to ingest incomplete point clouds and output completed ones from ODGNet

2. Srinik - Uptrans.py - Added batch normalization - changed around 50 lines Set up pipeline to preprocess data from ODGNet and ingest into FS-SDF.

3. Amaan Kayum - Data Generation and Movement scripts. test_extra sh files. train_text_extract.py changes around 30-40 lines Helped in integration of pipelines and modifying architectures

Create any venv and do below

pip install -r requirements.txt

Setup Libs Install pointnet2_ops_lib and Chamfer Distance in the extension Folder: python3 setup.py install (in both folders)

use "sh script.sh" to download into the "/root/ODGNet/data.zip" folder. assuming pwd as "/root/ODGNet" After download, unzip the files into a new folder data/ShapeNet55-34/shapenet_pc such that shapenet_pc folder will contain the preprocessed shapenet dataset

Create data/ShapeNet55-34/shapenet_subset as a sibling to shapenet_pc

python test_text_extract.py
python train_text_extract.py

The above python files will copy the selected subcatogories into the shapenet_subset which is used for training and testing

Please check the bash files, e.g., "sh train_55.sh" for the shapenet dataset.

"sh test.sh" for testing with the model localtion. model placed at the location : experiments/UpTrans/ShapeNet55_models/shape55_upTransb1/ckpt-epoch-050.pth

Create a conda environment as detailed in the readme of IFNet folder in fssdf code

FSSDF uses IFNet data preprocessing to generate the data. Now we move to the IfNet folder and download and create the data for preprocessing.

conda env create -f if-net_env.yml
conda activate if-net

Install the needed libraries with:

cd data_processing/libmesh/
python setup.py build_ext --inplace
cd ../libvoxelize/
python setup.py build_ext --inplace
cd ../..

The processing will take atleast 1-2Hrs and the the data will explode to approx 90GB Download the ShapeNet data preprocessed by [Xu et. al. NeurIPS'19] from here into the shapenet folder.

Now extract the files into shapenet\data with: (for cabinet and lamp)

ls shapenet/02933112.tar.gz |xargs -n1 -i tar -xf {} -C shapenet/data/
ls shapenet/03636649.tar.gz |xargs -n1 -i tar -xf {} -C shapenet/data/

The above downloads will be 3-4GB

Next, the inputs and training point samples for IF-Nets are created.

First, the data is converted to the .off-format and scaled using

python data_processing/convert_to_scaled_off.py

The input data for Voxel Super-Resolution of voxels is created with

python data_processing/voxelize.py -res 32

using -res 32 for 32³ and -res 128 for 128³ resolution.

The input data for Point Cloud Completion is created with

python data_processing/voxelized_pointcloud_sampling.py -res 128 -num_points 300

using -num_points 300 for point clouds with 300 points and -num_points 3000 for 3000 points.

To generate ground truth Signed distance values instead of occupancies run:

python data_processing/boundary_sampling_sdf.py -sigma 0.1
python data_processing/boundary_sampling_sdf.py -sigma 0.01

To start a training please run the following command:

python train.py -res 128 -pc_samples 3000 -epochs 100 -inner_steps 5 -batch_size 8

You can add the following options -p_enc and _p_dec to initialize the encoder and/or the decoder with a pretrained model. Also, you can freeze the encoder during the training by adding the option -freeze to your command.

The command:

python generate.py -res 128 -pc_samples 3000 -batch_size 8 -inner_steps 5 -exp <exp_name> -checkpoint <checkpoint>  

Where exp_name is the path to the folder containing the trained model checkpoints.

Please run

python data_processing/evaluate.py -reconst -generation_path experiments/iVoxels_dist-0.5_0.5_sigmas-0.1_0.01_v32_mShapeNet32Vox/evaluation_10_@256/generation/

to evaluate each reconstruction, where -generation_path is the path to the reconstructed objects generated in the previous step.

The above evaluation script can be run on multiple machines in parallel in order to increase generation speed significantly.

Then run

python data_processing/evaluate.py -voxels -res 32

to evaluate the quality of the input. For voxel girds use '-voxels' with '-res' to specify the input resolution and for point clouds use '-pc' with '-points' to specify the number of points.

The quantitative evaluation of all reconstructions and inputs are gathered and put into experiment/YOUR_EXPERIMENT/evaluation_CHECKPOINT_@256 using

python data_processing/evaluate_gather.py -voxel_input -res 32 -generation_path experiments/iVoxels_dist-0.5_0.5_sigmas-0.1_0.01_v32_mShapeNet32Vox/evaluation_10_@256/generation/

where you should use -voxel_input for Voxel Super-Resolution experiments, with -res specifying the input resolution or -pc_input for Point Cloud Completion, with -points specifying the number of points used.