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Pix2Vox

Language grade: Python Total alerts

This repository contains the source code for the paper Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images. The follow-up work Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images has been published in International Journal of Computer Vision (IJCV).

Overview

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 and Pix3D 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 (457.0 MB) Pix2Vox-F (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.

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