tourism_data.md

Dataset Description

One potential shortcoming of GL3D is that images are captured within a short period of time, thus lack of illumination/weather/season variations. Although photometric data augmentation could be applied, we still seek for more realistic data to improve the learning models.

To this end, following SiaMAC [1], we have also generated geometric labels from public Internet tourism datasets to further increase the data diversity. Precisely, we download and extract the images from retrieval-SfM-120k.mat, then reconstruct each data by our 3D engine, and finally obtain 530 scenes (55,657 images) that we consider are well-constructed (> 80% images are registered).

Downloads

The same protocols are defined for downloading the data. For dataset images:

Sources	Data Name	Chunk Start	Chunk End	Disk	Descriptions
tourism	tourism_raw_imgs	0	38	19G	Original images of tourism dataset
tourism	tourism_imgs	0	29	15G	1000x1000 undistorted images of tourism dataset

For geometric labels:

File Name	Data Name	Chunk Start	Chunk End	Disk	Task
geolabel/cameras.txt	tourism_cams	0	0	<0.1G	Common
img_kpts/<img_idx>.bin	tourism_kpts	0	12	6.0G	Common
depths/<img_idx>.pfm	tourism_depths	0	24	12G	Common
geolabel/corr.bin	tourism_corr	0	9	4.5G	Local descriptor
geolabel/mask.bin	tourism_mask	0	13	6.5G	Image retrieval
geolabel/common_track.txt	tourism_ct	0	0	<0.1G	Image retrieval
geolabel/mesh_overlap.txt	tourism_mo	0	0	<0.1G	Image retrieval