This is the code repository for "PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction" .
European Conference on Computer Vision 2018 (Oral presentation)
Created by Yifei Shi, Kai Xu, Matthias Niessner, Szymon Rusinkiewicz and Thomas Funkhouser
You can download the data here from the TUM cluster.
The code depends on Pytorch. Both Python 2.x and 3.x should work.
To train a model from scratch, run:
python train.py --train_root_dir TRAINING_TRIPLETS_DIR
Arguments:
'--epochs' (number of epochs; default=100)
'--batch_size' (batch size; default=16)
'--num_workers' (number of workers; default=8)
'--save_snapshot' (save snapshots of trained model)
'--save_snapshot_every' (save training log for every X frames; default=100)
'--lr' (initial learning rate; default=.001)
'--focal_loss_lambda' (the lambda in the focal loss; default=3)
'--gpu' (device id of GPU to run cuda; default=0)
'--train_csv_path' (the path of triplet_train.csv)
'--train_root_dir' (the folder path of training_triplets)
'--save_path' (trained model path, default='./models')
To extract feature for COP, run:
python test.py --test_root_dir COP_DIR
For example, if you want to extract feature for the positive data in COP-D1, use command:
python test.py --test_root_dir COP/COP-D1_pos
Arguments:
'--test_csv_path' (the path of triplet_test.csv)
'--test_root_dir' (the folder path of testing data)
'--feature_path' (feature extraction output path, default='./feature_extraction')
If you find PlaneMatch useful in your research, please cite:
@inproceedings{shi2018planematch,
author = {Yifei Shi and Kai Xu and Matthias Nie{\ss}ner and Szymon Rusinkiewicz and Thomas Funkhouser},
booktitle = {Proceedings of the European Conference on Computer Vision ({ECCV})},
title = {PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction},
year = {2018}
}