README file
Data Set Title: the Booster dataset – Monocular split
Data Set Author/s: Pierluigi Zama Ramirez (University of Bologna), 0000-0001-7734-5064; Fabio Tosi (University of Bologna), 0000-0002-6276-5282; Alex Costanzino (University of Bologna) 0000-0001-9859-8482, 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: 2023
Project Info: Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces, the project extend the previous Booster dataset that was 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 in a balanced setting, acquired by two cameras, namely 00-02. However, only camera_00 is released because the main scope of the dataset is to be used as official test set for monocular networks. Ground truths are withheld for the same reason. One main archive is provided, structured as follows:

Booster Mono
•	test_mono 										          folder 
It contains 21 testing sequences, each contained in a separate folder:
o	Microwave1 folder 
o	Window3 folder
o	...
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	...
•	calib_00-02.xml
Calibration file containing intrinsic/extrinsic parameters.
Data set Documentation
Abstract
Estimating depth from images nowadays yields outstanding results, both in terms of in-domain accuracy and generalization. However, we identify two main challenges that remain open in this field: dealing with non-Lambertian materials and effectively processing high-resolution images. Purposely, we propose a novel dataset that includes accurate and dense ground-truth labels at high resolution, featuring scenes containing several specular and transparent surfaces. Our acquisition pipeline leverages a novel deep space-time stereo framework, enabling easy and accurate labeling with sub-pixel precision. 

File specifics
RGB images are provided as png files. Calib_00-02.xml is an xml file containing calibration parameters. 
Notes 
The dataset is released without ground truth as it is used as test set.

Data sources 
Images have been collected with Sony IMX253LQR-C 12.4 Mpx sensors.

Methodologies 
Images can be opened with any python package among opencv, pil, etc.

List of variables