The availability of high-resolution data and accurate ground truth is essential to evaluate and compare methods and algorithms properly. Moreover, it is often difficult to acquire real data for a given application domain that is sufficiently representative and heterogeneous in terms of scene representation, acquisition conditions, point of view, etc. To overcome the limitations of available datasets, this paper presents a new synthetic, multi-purpose dataset called ENRICH for testing photogrammetric and computer vision algorithms. Compared to existing datasets, ENRICH offers higher resolution images rendered with different lighting conditions, camera orientations, scales, and fields of view. Specifically, ENRICH is composed of three sub-datasets: ENRICH-Aerial, ENRICH-Square, and ENRICH-Statue, each exhibiting different characteristics. We show the usefulness of the proposed dataset on several examples of photogrammetry and computer vision-related tasks such as: evaluation of hand-crafted and deep learning-based local features, effects of ground control points (GCPs) configuration on the 3D accuracy, and monocular depth estimation. We make ENRICH publicly available at: https://github.com/davidemarelli/ENRICH.

Marelli, D., Morelli, L., Farella, E., Bianco, S., Ciocca, G., Remondino, F. (2023). ENRICH: multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetry. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 198(April 2023), 84-98 [10.1016/j.isprsjprs.2023.03.002].

ENRICH: multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetry

Marelli, Davide
Primo
;
Bianco, Simone;Ciocca, Gianluigi
Co-ultimo
;
2023

Abstract

The availability of high-resolution data and accurate ground truth is essential to evaluate and compare methods and algorithms properly. Moreover, it is often difficult to acquire real data for a given application domain that is sufficiently representative and heterogeneous in terms of scene representation, acquisition conditions, point of view, etc. To overcome the limitations of available datasets, this paper presents a new synthetic, multi-purpose dataset called ENRICH for testing photogrammetric and computer vision algorithms. Compared to existing datasets, ENRICH offers higher resolution images rendered with different lighting conditions, camera orientations, scales, and fields of view. Specifically, ENRICH is composed of three sub-datasets: ENRICH-Aerial, ENRICH-Square, and ENRICH-Statue, each exhibiting different characteristics. We show the usefulness of the proposed dataset on several examples of photogrammetry and computer vision-related tasks such as: evaluation of hand-crafted and deep learning-based local features, effects of ground control points (GCPs) configuration on the 3D accuracy, and monocular depth estimation. We make ENRICH publicly available at: https://github.com/davidemarelli/ENRICH.
Articolo in rivista - Articolo scientifico
3D reconstruction; Dataset; Depth estimation; Image matching; Local features; Photogrammetry; Synthetic images;
English
10-mar-2023
2023
198
April 2023
84
98
none
Marelli, D., Morelli, L., Farella, E., Bianco, S., Ciocca, G., Remondino, F. (2023). ENRICH: multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetry. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 198(April 2023), 84-98 [10.1016/j.isprsjprs.2023.03.002].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/405277
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