Awesome
{J, T}-Linkage for Vanishing Point Estimation
This is an implementation of J-Linkage [1] and T-Linkage [2] for vanishing point estimation from line segments extracted via LSD [3].
This implementation was used in our CONSAC paper [4], so please cite the paper if you use this code:
@inproceedings{kluger2020consac,
title={CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus},
author={Kluger, Florian and Brachmann, Eric and Ackermann, Hanno and Rother, Carsten and Yang, Michael Ying and Rosenhahn, Bodo},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2020}
}
Setup
Assuming that you are using Anaconda.
Get the code:
git clone --recurse-submodules https://github.com/fkluger/vp-linkage.git
cd vp-linkage
git submodule update --init --recursive
Prepare environment:
conda env create -f environment.yml
source activate vp_linkage
cd datasets/nyu_vp/lsd
python setup.py build_ext --inplace
cd ../../yud_plus/lsd
python setup.py build_ext --inplace
cd ../../..
Datasets
NYU-VP
The vanishing point labels and pre-extracted line segments for the NYU dataset are fetched automatically via the nyu_vp submodule. In order to use the original RGB images as well, you need to obtain the original dataset MAT-file and convert it to a version 7 MAT-file in MATLAB so that we can load it via scipy:
load('nyu_depth_v2_labeled.mat')
save('nyu_depth_v2_labeled.v7.mat','-v7')
YUD and YUD+
Pre-extracted line segments and VP labels are fetched automatically via the yud_plus submodule. RGB images and camera
calibration parameters, however, are not included. Download the original York Urban Dataset from the
Elder Laboratory's website and
store it under the datasets/yud_plus/data
subfolder.
Demo
...coming soon(ish)
Run
To compute the AUC metric over the YUD test set, run:
python linkage.py --dataset yud --dataset_path ./datasets/yud_plus/data/
For YUD+:
python linkage.py --dataset yud+ --dataset_path ./datasets/yud_plus/data/
For NYU-VP:
python linkage.py --dataset nyu --dataset_path ./datasets/nyu_vp/data/ --mat_file_path nyu_depth_v2_labeled.v7.mat
Add the option --tlinkage
in order to switch from J-Linkage to T-Linkage.
See python linkage.py --help
for available options.
References
[1] Roberto Toldo and Andrea Fusiello. Robust multiple structures estimation with j-linkage. ECCV 2008.
[2] Luca Magri and Andrea Fusiello. T-linkage: A continuous relaxation of j-linkage for multi-model fitting. CVPR 2014.
[3] Rafael Grompone Von Gioi, Jeremie Jakubowicz, Jean-Michel Morel, and Gregory Randall. Lsd: A fast line segment detector with a false detection control. TPAMI 2008.
[4] Florian Kluger, Eric Brachmann, Hanno Ackermann, Carsten Rother, Michael Ying Yang, and Bodo Rosenhahn. CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus. CVPR 2020