dispersion-score

Official implementation of 3DV 2021 Paper A Dataset-dispersion Perspective on Reconstruction versus Recognition in Single-view 3D Reconstruction Networks

Dispersion Score is a data-driven metric that is used to measure the internel machaism of single-view 3D reconstruction network: the tendency of network to perform recognition or reconstruction. It can also be used to diagnose training data and guide data augmentation as a heuristic.

For more details, please see our paper.

Installation

To install dispersion-score and develop locally:

• PyTorch version >= 1.6.0
• Python version = 3.6

conda create -n dispersion_score python=3.6
conda activate dispersion_score
git clone https://github.com/YefanZhou/dispersion-score.git
cd dispersion-score
chmod +x setup.sh 
./setup.sh

Dataset

Download provided synthetic dataset and customized ShapeNet renderings as following, or you may build synthetic dataset or build renderings yourself.

bash download/download_data.sh

Manually download ShapeNet V1 (AtlasNet version): pointclouds, renderings , and unzip the two files as following.

unzip ShapeNetV1PointCloud.zip -d ./dataset/data/
unzip ShapeNetV1Renderings.zip -d ./dataset/data/

Experiments Results

Download our trained models:

bash download/download_checkpts.sh

Experiments on Synthetic datasets:

Measure Dispersion Score (DS) and Visualize Measurements

python eval_scripts/eval_ds_synthetic.py --gpus [IDS OF GPUS TO USE]

Run the notebook to visualize the results and reproduce plots.

Model Training

You could also train models from scratch as following instead of using trained models.

python train_scripts/train_synthetic.py --gpus [IDS OF GPUS TO USE]

Experiments on ShapeNet:

Measure Dispersion Score (DS) and Visualize Measurements

# More dispersed training Images 
python eval_scripts/eval_ds_moreimgs.py --gpus [IDS OF GPUS TO USE]

# More dispersed training shapes 
python eval_scripts/eval_ds_moreshapes.py --gpus [IDS OF GPUS TO USE] 

Run the notebook to visualize the results and reproduce plots.

Model Training

You could also train models from scratch as following instead of using trained models.

python train_scripts/train_more_imgs.py --gpus [IDS OF GPUS TO USE]
python train_scripts/train_more_shapes.py --gpus [IDS OF GPUS TO USE]