An Immersive Multi-Elevation Multi-Seasonal Dataset for 3D Reconstruction and Visualization

Autor:	Liu, Xijun, Zhou, Yifan, Guo, Yuxiang, Chellappa, Rama, Peng, Cheng
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning I.4.1 I.4.5
Druh dokumentu:	Working Paper
Popis:	Significant progress has been made in photo-realistic scene reconstruction over recent years. Various disparate efforts have enabled capabilities such as multi-appearance or large-scale modeling; however, there lacks a welldesigned dataset that can evaluate the holistic progress of scene reconstruction. We introduce a collection of imagery of the Johns Hopkins Homewood Campus, acquired at different seasons, times of day, in multiple elevations, and across a large scale. We perform a multi-stage calibration process, which efficiently recover camera parameters from phone and drone cameras. This dataset can enable researchers to rigorously explore challenges in unconstrained settings, including effects of inconsistent illumination, reconstruction from large scale and from significantly different perspectives, etc. Comment: 4 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.14418 Zobrazit plný text záznamu View this record from Arxiv