An Immersive Multi-Elevation Multi-Seasonal Dataset for 3D Reconstruction and Visualization
This provides a new benchmark for researchers in 3D reconstruction and visualization to test methods in unconstrained settings, though it is incremental as it builds on existing dataset efforts.
The authors tackled the lack of a comprehensive dataset for evaluating holistic scene reconstruction by introducing a multi-elevation, multi-seasonal imagery collection of the Johns Hopkins Homewood Campus, enabling research on challenges like inconsistent illumination and large-scale reconstruction.
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.