README file
Data Set Title: the Booster dataset
Data Set Author/s: Pierluigi Zama Ramirez (University of Bologna), 0000-0001-7734-5064; Fabio Tosi (University of Bologna), 0000-0002-6276-5282; Matteo Poggi (University of Bologna), 0000-0002-3337-2236; Samuele Salti (University of Bologna), 0000-0001-5609-426X; Stefano Mattoccia (University of Bologna), 0000-0002-3681-7704; Luigi Di Stefano (University of Bologna), 0000-0001-6014-6421;

Data Set Contact Person/s: Samuele Salti (University of Bologna), 0000-0001-5609-426X, samuele.salti@unibo.it; Stefano Mattoccia (University of Bologna), 0000-0002-3681-7704, stefano.mattoccia@unibo.it; Luigi Di Stefano (University of Bologna), 0000-0001-6014-6421, luigi.distefano@unibo.it;

Data Set License: this data set is distributed under a 
Creative Commons Attribution Non-Commercial 4.0 International license
Publication Year: 2022
Project Info: Open Challenges in Deep Stereo: the Booster dataset, funded by HUAWEI TECHNOLOGIES OY (FINLAND); https://cvlab-unibo.github.io/booster-web . 
Data set Contents
The data set consists of a set of images collected in different environments. In each environment, images are collected both in balanced and unbalanced settings, acquired by camera 00-02 and 00-01 pairs respectively.  Two main archives are provided, structured as follows:

Booster Labeled
•	train folder
It contains 38 training sequences, in both balanced and unbalanced setup
o	balanced folder
It contains training images acquired in balanced setup
•	Bathroom folder
•	Bedroom folder
•	...
o	unbalanced folder
It contains training images acquired in balanced setup
•	Bathroom folder
•	Bedroom folder
•	...
•	test folder 
It contains 26 testing sequences, in both balanced and unbalanced setup
o	balanced folder
It contains testing images acquired in balanced setup (ground-truth is withheld)
•	Balance folder 
•	Barrel folder
•	...
o	unbalanced folder
It contains testing images acquired in unbalanced setup (ground-truth is withheld)
•	Balance folder 
•	Barrel folder
•	...

Booster unlabeled
•	balanced folder
It contains training images acquired in balanced setup
•	unbalanced folder
It contains training images acquired in unbalanced setup
Data set Documentation
Abstract
We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the presence of several specular and transparent surfaces, i.e. the main causes of failures for state-of-the-art stereo networks. Our acquisition pipeline leverages a novel deep space-time stereo framework which allows for easy and accurate labeling with sub-pixel precision. We release a total of 419 samples collected in 64 different scenes and annotated with dense ground-truth disparities. Each sample include a high-resolution pair (12 Mpx) as well as an unbalanced pair (Left: 12 Mpx, Right: 1.1 Mpx). Additionally, we provide manually annotated material segmentation masks and 15K unlabeled samples.  We evaluate state-of-the-art deep networks based on our dataset, highlighting their limitations in addressing the open challenges in stereo and drawing hints for future research.
Each sequence folder contains (a subset of) the following folders and files:
•	camera_00 folder
It contains left camera images, under different illumination
o	im1.png
o	...
•	camera_0X folder
It contains right camera images, under different illumination (X=2 balanced, X=1 unbalanced)
o	im1.png
o	...
•	calib_00-0X.xml
Calibration file containing intrinsic/extrinsic parameters (X=2 balanced, X=1 unbalanced)
•	disp_00.npy
Left ground-truth disparity map
•	disp_02.npy
Right ground-truth disparity map
•	mask_00.png
Occlusion mask for left ground-truth disparity map (balanced setting only)
•	mask_02.png
Occlusion mask for right ground-truth disparity map (balanced setting only)
•	mask_cat.png
Materials mask for left ground-truth disparity map (balanced setting only)
•	mask_cat_col.png
Colored materials mask for left ground-truth disparity map (balanced setting only)
•	warped_mask_cat.png
Materials mask for left ground-truth disparity map (unbalanced setting only)
•	warped_mask_cat_col.png
Colored materials mask for left ground-truth disparity map (unbalanced setting only)

File specifics
Ground-truth disparity map are provided as .npy files, encoding floating point disparities. Occlusion masks are provided as binary .png images, encoding valid and occluded pixels respectively with 255 and 0. Materials masks are provided as .png images, encoding pixels belonging to different material classes with 0, 1, 2 and 3 values.
Notes 
The dataset is release in two main archives: 
•	labeled examples, providing both training images (for which ground-truth annotations are provided) and testing images (for which ground-truth annotations are withheld).
•	unlabeled examples, for which balanced and unbalanced stereo images are provided without any annotation.

Data sources 
High-resolution images have been collected with Sony IMX253LQR-C 12.4 Mpx sensors.
Low-resolution images have been collected with Sony IMX174LQJ-C 2.3 Mpx sensor.

Methodologies 
Images can be opened with any python package among opencv, pil, etc.
Ground-truth disparity maps can be opened with numpy.

List of variables