This is a Tensorflow implementation of "SuperPoint: Self-Supervised Interest Point Detection and Description." Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich. ArXiv 2018.
HPatches repeatability results
Repeatability on HPatches computed with 300 points detected in common between pairs of images and with a NMS of 4:
|Illumination changes||Viewpoint changes|
|SuperPoint (our implementation)||0.661||0.409|
|SuperPoint (pretrained model of MagicLeap)||0.641||0.379|
NOTE: this work is still in progress. Although we match the results of the original paper for illumination changes, more experiments are required to 1) improve the robustness to viewpoint changes, and 2) train the descriptor. We provide a temporary Tensorflow SavedModel of MagicPoint in
make install # install the Python requirements and setup the paths
Python 3.6.1 is required. You will be asked to provide a path to an experiment directory (containing the training and prediction outputs, referred as
$EXPER_DIR) and a dataset directory (referred as
$DATA_DIR). Create them wherever you wish and make sure to provide their absolute paths.
MS-COCO 2014 and HPatches should be downloaded into
$DATA_DIR. The Synthetic Shapes dataset will also be generated there. The folder structure should look like:
$DATA_DIR |-- COCO | |-- train2014 | | |-- file1.jpg | | `-- ... | `-- val2014 | |-- file1.jpg | `-- ... `-- HPatches | |-- i_ajuntament | `-- ... `-- synthetic_shapes # will be automatically created
All commands should be executed within the
superpoint/ subfolder. When training a model or exporting its predictions, you will often have to change the relevant configuration file in
superpoint/configs/. Both multi-GPU training and export are supported.
1) Training MagicPoint on Synthetic Shapes
python experiment.py train configs/magic-point_shapes.yaml magic-point_synth
magic-point_synth is the experiment name, which may be changed to anything. The training can be interrupted at any time using
Ctrl+C and the weights will be saved in
$EXPER_DIR/magic-point_synth/. The Tensorboard summaries are also dumped there. When training for the first time, the Synthetic Shapes dataset will be generated.
2) Exporting detections on MS-COCO
python export_detections.py configs/magic-point_coco_export.yaml magic-point_synth --pred_only --batch_size=5 --export_name=magic-point_coco-export1
This will save the pseudo-ground truth interest point labels to
$EXPER_DIR/outputs/magic-point_coco-export1/. You might enable or disable the Homographic Adaptation in the configuration file.
3) Training MagicPoint on MS-COCO
python experiment.py train configs/magic-point_coco_train.yaml magic-point_coco
You will need to indicate the paths to the interest point labels in
magic-point_coco_train.yaml by setting the entry
data/labels, for example to
outputs/magic-point_coco-export1. You might repeat steps 2) and 3) several times.
4) Evaluating the repeatability on HPatches
python export_detections_repeatability.py configs/magic-point_repeatability.yaml magic-point_coco --export_name=magic-point_hpatches-repeatability-v
You will need to decide whether you want to evaluate for viewpoint or illumination by setting the entry
data/alteration in the configuration file. The predictions of the image pairs will be saved in
$EXPER_DIR/outputs/magic-point_hpatches-repeatability-v/. To proceed to the evaluation, head over to
notebooks/detector_repeatability_coco.ipynb. You can also evaluate the repeatbility of the classical detectors using the configuration file
5) Validation on MS-COCO
It is also possible to evaluate the repeatability on a validation split of COCO. You will first need to generate warped image pairs using