An Immersive Multi-Elevation Multi-Seasonal Dataset for 3D Reconstruction and Visualization
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912161189593088 |
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| author | Liu, Xijun Zhou, Yifan Guo, Yuxiang Chellappa, Rama Peng, Cheng |
| author_facet | Liu, Xijun Zhou, Yifan Guo, Yuxiang Chellappa, Rama Peng, Cheng |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_14418 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Immersive Multi-Elevation Multi-Seasonal Dataset for 3D Reconstruction and Visualization Liu, Xijun Zhou, Yifan Guo, Yuxiang Chellappa, Rama Peng, Cheng Computer Vision and Pattern Recognition Machine Learning I.4.1, I.4.5 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. |
| title | An Immersive Multi-Elevation Multi-Seasonal Dataset for 3D Reconstruction and Visualization |
| topic | Computer Vision and Pattern Recognition Machine Learning I.4.1, I.4.5 |
| url | https://arxiv.org/abs/2412.14418 |