A multi-camera dataset for depth estimation in an indoor scenario

Giulio Marin; Gianluca Agresti; Ludovico Minto; Pietro Zanuttigh

doi:10.1016/j.dib.2019.104619

A multi-camera dataset for depth estimation in an indoor scenario

Data Brief. 2019 Oct 7:27:104619. doi: 10.1016/j.dib.2019.104619. eCollection 2019 Dec.

Authors

Giulio Marin¹, Gianluca Agresti¹, Ludovico Minto¹, Pietro Zanuttigh¹

Affiliation

¹ University of Padova, Italy.

Abstract

Time-of-Flight (ToF) sensors and stereo vision systems are two of the most diffused depth acquisition devices for commercial and industrial applications. They share complementary strengths and weaknesses. For this reason, the combination of data acquired from these devices can improve the final depth estimation accuracy. This paper introduces a dataset acquired with a multi-camera system composed by a Microsoft Kinect v2 ToF sensor, an Intel RealSense R200 active stereo sensor and a Stereolabs ZED passive stereo camera system. The acquired scenes include indoor settings with different external lighting conditions. The depth ground truth has been acquired for each scene of the dataset using a line laser. The data can be used for developing fusion and denoising algorithms for depth estimation and test with different lighting conditions. A subset of the data has already been used for the experimental evaluation of the work "Stereo and ToF Data Fusion by Learning from Synthetic Data".

Keywords: Active stereo; Data fusion; Depth estimation; Stereo vision; Time-of-Flight.