PlanarReconstruction
PyTorch implementation of our CVPR 2019 paper:
Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding
Zehao Yu*, Jia Zheng*, Dongze Lian, Zihan Zhou, Shenghua Gao
(* Equal Contribution)
Getting Started
Installation
Clone repository and use git-lfs to fetch the trained model (or download here):
git clone [email protected]:svip-lab/PlanarReconstruction.gitWe use Python 3. Create an Anaconda enviroment and install the dependencies:
conda create -y -n plane python=3.6
conda activate plane
conda install -c menpo opencv
pip install -r requirements.txtDownloading and converting data
Please download the .tfrecords files for training and testing converted by PlaneNet, then convert the .tfrecords to .npz files:
python data_tools/convert_tfrecords.py --data_type=train --input_tfrecords_file=/path/to/planes_scannet_train.tfrecords --output_dir=/path/to/save/processd/data
python data_tools/convert_tfrecords.py --data_type=val --input_tfrecords_file=/path/to/planes_scannet_val.tfrecords --output_dir=/path/to/save/processd/dataTraining
Run the following command to train our network:
python main.py train with dataset.root_dir=/path/to/save/processd/dataEvaluation
Run the following command to evaluate the performance:
python main.py eval with dataset.root_dir=/path/to/save/processd/data resume_dir=/path/to/pretrained.pt dataset.batch_size=1Prediction
Run the following command to predict on a single image:
python predict.py with resume_dir=pretrained.pt image_path=/path/to/imageAcknowledgements
We thank Chen Liu for his great works and repos.
Citation
Please cite our paper for any purpose of usage.
@inproceedings{YuZLZG19,
author = {Zehao Yu and Jia Zheng and Dongze Lian and Zihan Zhou and Shenghua Gao},
title = {Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding},
booktitle = {CVPR},
pages = {1029--1037},
year = {2019}
}