https://github.com/hzxie/pix2vox
The official implementation of "Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images". (ICCV 2019)
https://github.com/hzxie/pix2vox
3d-object-reconstruction 3d-reconstruction iccv2019 shapenet voxel
Last synced: 6 months ago
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The official implementation of "Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images". (ICCV 2019)
- Host: GitHub
- URL: https://github.com/hzxie/pix2vox
- Owner: hzxie
- License: mit
- Created: 2018-04-15T12:26:00.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-01-23T08:11:46.000Z (over 1 year ago)
- Last Synced: 2025-03-29T01:12:44.637Z (6 months ago)
- Topics: 3d-object-reconstruction, 3d-reconstruction, iccv2019, shapenet, voxel
- Language: Python
- Homepage: https://haozhexie.com/project/pix2vox
- Size: 1.19 MB
- Stars: 503
- Watchers: 14
- Forks: 118
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Pix2Vox
[](https://codebeat.co/projects/github-com-hzxie-pix2vox-master)
This repository contains the source code for the paper [Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images](https://arxiv.org/abs/1901.11153). The follow-up work [Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images](https://arxiv.org/abs/2006.12250) has been published in *International Journal of Computer Vision (IJCV)*.
## Cite this work
```
@inproceedings{xie2019pix2vox,
title={Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images},
author={Xie, Haozhe and
Yao, Hongxun and
Sun, Xiaoshuai and
Zhou, Shangchen and
Zhang, Shengping},
booktitle={ICCV},
year={2019}
}
```
## Datasets
We use the [ShapeNet](https://www.shapenet.org/) and [Pix3D](http://pix3d.csail.mit.edu/) datasets in our experiments, which are available below:
- ShapeNet rendering images: http://cvgl.stanford.edu/data2/ShapeNetRendering.tgz
- ShapeNet voxelized models: http://cvgl.stanford.edu/data2/ShapeNetVox32.tgz
- Pix3D images & voxelized models: http://pix3d.csail.mit.edu/data/pix3d.zip
## Pretrained Models
The pretrained models on ShapeNet are available as follows:
- [Pix2Vox-A](https://gateway.infinitescript.com/?fileName=Pix2Vox-A-ShapeNet.pth) (457.0 MB)
- [Pix2Vox-F](https://gateway.infinitescript.com/?fileName=Pix2Vox-F-ShapeNet.pth) (29.8 MB)
## Prerequisites
#### Clone the Code Repository
```
git clone https://github.com/hzxie/Pix2Vox.git
```
#### Install Python Denpendencies
```
cd Pix2Vox
pip install -r requirements.txt
```
#### Update Settings in `config.py`
You need to update the file path of the datasets:
```
__C.DATASETS.SHAPENET.RENDERING_PATH = '/path/to/Datasets/ShapeNet/ShapeNetRendering/%s/%s/rendering/%02d.png'
__C.DATASETS.SHAPENET.VOXEL_PATH = '/path/to/Datasets/ShapeNet/ShapeNetVox32/%s/%s/model.binvox'
__C.DATASETS.PASCAL3D.ANNOTATION_PATH = '/path/to/Datasets/PASCAL3D/Annotations/%s_imagenet/%s.mat'
__C.DATASETS.PASCAL3D.RENDERING_PATH = '/path/to/Datasets/PASCAL3D/Images/%s_imagenet/%s.JPEG'
__C.DATASETS.PASCAL3D.VOXEL_PATH = '/path/to/Datasets/PASCAL3D/CAD/%s/%02d.binvox'
__C.DATASETS.PIX3D.ANNOTATION_PATH = '/path/to/Datasets/Pix3D/pix3d.json'
__C.DATASETS.PIX3D.RENDERING_PATH = '/path/to/Datasets/Pix3D/img/%s/%s.%s'
__C.DATASETS.PIX3D.VOXEL_PATH = '/path/to/Datasets/Pix3D/model/%s/%s/%s.binvox'
```
## Get Started
To train Pix2Vox, you can simply use the following command:
```
python3 runner.py
```
To test Pix2Vox, you can use the following command:
```
python3 runner.py --test --weights=/path/to/pretrained/model.pth
```
If you want to train/test Pix2Vox-F, you need to checkout to `Pix2Vox-F` branch first.
```
git checkout -b Pix2Vox-F origin/Pix2Vox-F
```
## License
This project is open sourced under MIT license.