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GeoDesc implementation

TensorFlow implementation of GeoDesc for ECCV'18 paper "GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints", Zixin Luo, Tianwei Shen, Lei Zhou, Siyu Zhu, Runze Zhang, Yao Yao, Tian Fang and Long Quan.

Update 4/13/2020

The training code is released in a separate project, TFMatch, which also contains two follow-up works (ContextDesc, CVPR'19 and ASLFeat, CVPR'20).

Update 12/05/2019

Add three options (--pyr_off, --ori_off, --half_sigma) to help to better understand the performance. You may use the new test pair (test_img3.png, test_img4.png) to validate their influence.

--pyr_off (False by default): disable the pyramid construction so as to sample patches from the original image, which boosts the performance when scale change is not significant.

--ori_off (False by default): disable the orientation estimation by SIFT, which boosts the performance when the rotation change is not significant.

--half_sigma (True by default): halve the sigma value in pyramid construction, which empirically boosts the performance.

Some recent findings about GeoDesc: as described in the paper, we trained GeoDesc with random rotations up to 90°. Different from the setting in HardNet, we perturbed each training patch individually by a random angle uniformly sampled over [-90°, 90°], which leaded to a maximum of 180° rotation difference for an input patch pair. We have found such augmentation helped to acquire stronger rotational invariance, which is important in our target applications, i.e., 3D reconstruction from aerial images. However, in general cases where images are upright-aligned regarding their EXIF orientations, training with smaller rotation perturbation will give notably better performance.

Update 08/04/2019

We improve the patch cropping implementation and now it gets 5 times faster.

Update 04/12/2018

Part of the training data is released in GL3D. We will constantly update dataset to help to reproduce the work. Please also cite MIRorR if you find this dataset useful for your research.

Update 14/08/2018

We have provided an example to test the matching performance of GeoDesc (examples/ See usage below.


Please use Python 2.7, install NumPy, OpenCV and TensorFlow. To run the image matching example, you may also need to compile opencv_contrib to get SIFT support.

Pre-trained model

Pre-trained GeoDesc model (in TensorFlow Protobuf format) can be found here.

Model in NumPy dictionary is available here, which is more handy to be converted to other formats.

Example scripts

1. Extract features of HPatches

An example script is provided to extract features of HPatches. HPatches should be ready in its original format.

After download HPatches, you can start to evaluate GeoDesc:

git clone
cd geodesc/model
cd ../examples
python \
    --hpatches_root=<hpatches_benchmark>/data/hpatches-release \

After the extraction, you can use HPatches benchmarking tools to evaluate GeoDesc (on split 'full' as GeoDesc is not trained on HPatches):

cd <hpatches_benchmark>/python
python --descr-name='geodesc' \
    --task=verification --task=matching --task=retrieval --split=full

And then display the results:

python --descr-name='geodesc' \
    --task=verification --task=matching --task=retrieval --split=full --results-dir=

2. Test image matching

As described in the paper, the matching pipeline consists of: i) detect keypoints by SIFT detector, ii) crop patches in the scale space, iii) compute features on cropped patches, and iv) match the two images. If you want to achieve the efficiency reported in the paper and use it for large-scale matching tasks, we strongly suggest you implementing the pipeline in C++ with integrated GPU-based SIFT (e.g., SIFTGPU) and GPU-based matcher (e.g., OpenCV GPU matcher). We have provided here only a prototype for research purposes.

To get started, simply run:

cd examples
python --cf_sift

The matching results from SIFT (top) and GeoDesc (bottom) will be displayed. Type python --h to view more options and test on your own images.


(Image source: Graffiti sequence in Heinly benchmark)

Training code

The ground truth patches used to train GeoDesc are under preparation.

Benchmark on HPatches, mAP


Benchmark on Heinly benchmark


Benchmark on ETH local features benchmark


Application on 3D reconstructions



Implementation of ECCV'18 paper - GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints





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