Start by downloading the InLoc_demo code. Once it is up and running according to the official instruction, you can copy and paste all the files available here overwriting the Features_WUSTL and parfor_sparseGV functions. generate_list.m will generate image_list.txt containing the queries and top 100 database matches (run sort -u image_list.txt > image_list_unique.txt to remove the duplicates). After extracting features for all the images in image_list_unique.txt, you can run custom_demo directly.

The feature extraction part for D2-Net can be done using the following command: python extract_features.py --image_list_file /path/to/image_list_unique.txt --multiscale --output_format .mat.

In case you plan on using your own features, don't forget to change the extension in Features_WUSTL.m. The local features are supposed to be stored in the mat format with two fields:

• keypoints - N x 3 matrix with x, y, scale coordinates of each keypoint in COLMAP format (the X axis points to the right, the Y axis to the bottom),

• descriptors - N x D matrix with the descriptors.

The evaluation pipeline is live at visuallocalization.net. In order to generate a submission file, please use the provided ImgList2text function.

We have also provided the merge_files MATLAB script that was used to merge the solutions of D2-Net Multiscale and Dense InLoc based on the view synthesis score. It can be used as follows merge_files('output/densePV_top10_shortlist_method1.mat', 'outputs/densePV_top10_shortlist_method2.mat').