Awesome
Homography patches dataset
This repository contains the code for evaluating feature descriptors
on the HPatches dataset. For more information on the methods and the
evaluation protocols please check [1].
Benchmark implementations
We provide two implementations for computing results on the HPatches
dataset, one in python and one in matlab.
python | matlab |
|---|---|
| details | details |
Benchmark tasks
Details about the benchmarking tasks can he found
here.
For a more in-depth description, please see the CVPR
2017 paper [1].
Getting the dataset
The data required for the benchmarks are saved in the ./data folder,
and are shared between the two implementations.
To download the HPatches image dataset, run the provided shell script
with the hpatches argument.
sh download.sh hpatches
To download the pre-computed files of a baseline descriptor X on the
HPatches dataset, run the provided download.sh script with the
descr X argument.
To see a list of all the currently available descriptor file results,
run scipt with only the descr argument.
sh download.sh descr # prints all the currently available baseline pre-computed descriptors
sh download.sh descr sift # downloads the pre-computed descriptors for sift
The HPatches dataset is saved on ./data/hpatches-release and the pre-computed descriptor files are saved on ./data/descriptors.
Dataset description
After download, the folder ../data/hpatches-release contains all the
patches from the 116 sequences. The sequence folders are named with
the following convention
i_X: patches extracted from image sequences with illumination changesv_X: patches extracted from image sequences with viewpoint changes
For each image sequence, we provide a set of reference patches
ref.png. For the remaining 5 images in the sequence, we provide
three patch sets eK.png and hK.png and tK.png, containing the
corresponding patches from ref.png as found in the K-th image with
increasing amounts of geometric noise (e<h<t).
Please see the patch extraction method details for more information about the extraction process.
References
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[1] HPatches: A benchmark and evaluation of handcrafted and learned local descriptors, Vassileios Balntas*, Karel Lenc*, Andrea Vedaldi and Krystian Mikolajczyk, CVPR 2017. *Authors contributed equally.
You might also be interested in the 3D reconstruction benchmark by Schönberger et al. also presented at CVPR 2017.