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

TL;DR

This paper introduces a comprehensive multi-seasonal, multi-elevation dataset of the Johns Hopkins campus for advancing 3D scene reconstruction and visualization research, addressing the lack of holistic evaluation datasets.

Contribution

It provides a large-scale, multi-condition dataset with calibrated imagery from various devices, enabling evaluation of scene reconstruction methods under diverse real-world conditions.

Findings

Dataset captures diverse environmental conditions and perspectives.

Calibration process ensures accurate camera parameter recovery.

Supports research on large-scale and multi-seasonal scene reconstruction.

Abstract

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.