GRNet

This repository contains the source code for the paper GRNet: Gridding Residual Network for Dense Point Cloud Completion.

Cite this work

@inproceedings{xie2020grnet,
  title={GRNet: Gridding Residual Network for Dense Point Cloud Completion},
  author={Xie, Haozhe and 
          Yao, Hongxun and 
          Zhou, Shangchen and 
          Mao, Jiageng and 
          Zhang, Shengping and 
          Sun, Wenxiu},
  booktitle={ECCV},
  year={2020}
}

Datasets

We use the ShapeNet, Compeletion3D, and KITTI datasets in our experiments, which are available below:

• ShapeNet
• Completion3D
• KITTI

Pretrained Models

The pretrained models on ShapeNet are available as follows:

• GRNet for ShapeNet (306.8 MB)
• GRNet for KITTI (306.8 MB)

Prerequisites

Clone the Code Repository

git clone https://github.com/hzxie/GRNet.git

Install Python Denpendencies

cd GRNet
pip install -r requirements.txt

Build PyTorch Extensions

NOTE: PyTorch >= 1.4, CUDA >= 9.0 and GCC >= 4.9 are required.

GRNET_HOME=`pwd`

# Chamfer Distance
cd $GRNET_HOME/extensions/chamfer_dist
python setup.py install --user

# Cubic Feature Sampling
cd $GRNET_HOME/extensions/cubic_feature_sampling
python setup.py install --user

# Gridding & Gridding Reverse
cd $GRNET_HOME/extensions/gridding
python setup.py install --user

# Gridding Loss
cd $GRNET_HOME/extensions/gridding_loss
python setup.py install --user

Preprocess the ShapeNet dataset

cd $GRNET_HOME/utils
python lmdb_serializer.py /path/to/shapenet/train.lmdb /path/to/output/shapenet/train
python lmdb_serializer.py /path/to/shapenet/valid.lmdb /path/to/output/shapenet/val

You can download the processed ShapeNet dataset here.

Update Settings in config.py

You need to update the file path of the datasets:

__C.DATASETS.COMPLETION3D.PARTIAL_POINTS_PATH    = '/path/to/datasets/Completion3D/%s/partial/%s/%s.h5'
__C.DATASETS.COMPLETION3D.COMPLETE_POINTS_PATH   = '/path/to/datasets/Completion3D/%s/gt/%s/%s.h5'
__C.DATASETS.SHAPENET.PARTIAL_POINTS_PATH        = '/path/to/datasets/ShapeNet/ShapeNetCompletion/%s/partial/%s/%s/%02d.pcd'
__C.DATASETS.SHAPENET.COMPLETE_POINTS_PATH       = '/path/to/datasets/ShapeNet/ShapeNetCompletion/%s/complete/%s/%s.pcd'
__C.DATASETS.KITTI.PARTIAL_POINTS_PATH           = '/path/to/datasets/KITTI/cars/%s.pcd'
__C.DATASETS.KITTI.BOUNDING_BOX_FILE_PATH        = '/path/to/datasets/KITTI/bboxes/%s.txt'

# Dataset Options: Completion3D, ShapeNet, ShapeNetCars, KITTI
__C.DATASET.TRAIN_DATASET                        = 'ShapeNet'
__C.DATASET.TEST_DATASET                         = 'ShapeNet'

Get Started

To train GRNet, you can simply use the following command:

python3 runner.py

To test GRNet, you can use the following command:

python3 runner.py --test --weights=/path/to/pretrained/model.pth

License

This project is open sourced under MIT license.