A Spatial Calibrated and Colour Corrected Light Field Outdoor Video Dataset from a 5x5 Dense Camera Array

被引：0

作者：

Wang, Yixiao ^{[1
]}

Mehajabin, Nusrat ^{[1
]}

Tohidypour, Hamid Reza ^{[1
]}

Song, Jerry ^{[1
]}

Huang, Menghong ^{[1
]}

Babaghorbani, Behnoosh ^{[1
]}

Chen, Zuhao ^{[1
]}

Pourazad, Mahsa T. ^{[1
]}

Nasiopoulos, Panos ^{[1
]}

Leung, Victor C. M. ^{[1
]}

机构：

[1] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC, Canada

来源：

2023 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, ISCAS | 2023年

关键词：

light field; plenoptic; dataset; dense camera array; autonomous driving; immersive media;

D O I：

10.1109/ISCAS46773.2023.10181775

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a new and calibrated light field (LF) video dataset is introduced, which focuses on outdoor scenes and objects. Each video stream is 10 seconds long and it is captured with a dense camera array that consists of 5x5 camera modules in 1640x1232 resolution at 40 frames per second. As multiple cameras in an array setup may suffer from various conditions of camera settings, lens structure, and lighting variations, the resulting images can be negatively affected by geometric distortion and colour difference. To address that, a unified calibration method involving both spatial calibration and colour correction is employed to correct inconsistences and achieve a better image quality with reduced image distortion. This video dataset would be suitable for further research and investigation of a variety LF applications, such as autonomous driving and immersive media.

引用

页数：5