Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/hzxie/grnet
The official implementation of "GRNet: Gridding Residual Network for Dense Point Cloud Completion". (ECCV 2020)
https://github.com/hzxie/grnet
3d-object-reconstruction aten cubic-feature-sampling eccv-2020 gridding point-cloud-completion pytorch
Last synced: 6 days ago
JSON representation
The official implementation of "GRNet: Gridding Residual Network for Dense Point Cloud Completion". (ECCV 2020)
- Host: GitHub
- URL: https://github.com/hzxie/grnet
- Owner: hzxie
- License: mit
- Created: 2019-08-02T09:21:53.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-01-23T08:12:15.000Z (about 1 year ago)
- Last Synced: 2025-01-29T05:09:17.152Z (14 days ago)
- Topics: 3d-object-reconstruction, aten, cubic-feature-sampling, eccv-2020, gridding, point-cloud-completion, pytorch
- Language: Python
- Homepage: https://haozhexie.com/project/grnet
- Size: 1.19 MB
- Stars: 314
- Watchers: 2
- Forks: 56
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# GRNet
This repository contains the source code for the paper [GRNet: Gridding Residual Network for Dense Point Cloud Completion](https://arxiv.org/abs/2006.03761).
[](https://codebeat.co/projects/github-com-hzxie-grnet-master)
## 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](https://www.shapenet.org/), [Compeletion3D](http://completion3d.stanford.edu/), and [KITTI](http://www.cvlibs.net/datasets/kitti/) datasets in our experiments, which are available below:
- [ShapeNet](https://drive.google.com/drive/folders/1P_W1tz5Q4ZLapUifuOE4rFAZp6L1XTJz)
- [Completion3D](http://download.cs.stanford.edu/downloads/completion3d/dataset2019.zip)
- [KITTI](https://drive.google.com/drive/folders/1fSu0_huWhticAlzLh3Ejpg8zxzqO1z-F)
## Pretrained Models
The pretrained models on ShapeNet are available as follows:
- [GRNet for ShapeNet](https://gateway.infinitescript.com/?fileName=GRNet-ShapeNet.pth) (306.8 MB)
- [GRNet for KITTI](https://gateway.infinitescript.com/?fileName=GRNet-KITTI.pth) (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](https://gateway.infinitescript.com/?fileName=ShapeNetCompletion).
#### 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.