构建了多季节多视角校园数据集，助力真实场景三维重建研究。
An Immersive Multi-Elevation Multi-Seasonal Dataset for 3D Reconstruction and Visualization
- 采集约翰霍普金斯校园多季节、多时段、多高度影像。
- 通过多阶段标定，从手机与无人机相机中恢复精确相机参数。
- 适合研究光照不一致、大范围建模等复杂场景重建问题。
近年来，逼真场景重建取得了显著进展，涵盖多外观和大规模建模等能力；然而，缺乏一个设计完善的基准数据集来全面评估重建技术的综合性能。本文介绍了约翰霍普金斯大学霍姆伍德校区的影像集合，该数据集在不同季节、一天中不同时段、多个高程下以及大尺度范围内采集。我们采用多阶段标定流程，从手机和无人机相机中高效恢复相机参数。该数据集可使研究人员在非约束环境下深入探索光照不一致、大尺度重建及显著视角差异等挑战。
原文摘要 · Abstract (English)
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.
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