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

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

Authors :
Marelli, D
Morelli, L
Farella, E
Bianco, S
Ciocca, G
Remondino, F
Marelli, Davide
Morelli, Luca
Farella, Elisa Mariarosaria
Bianco, Simone
Ciocca, Gianluigi
Remondino, Fabio
Marelli, D
Morelli, L
Farella, E
Bianco, S
Ciocca, G
Remondino, F
Marelli, Davide
Morelli, Luca
Farella, Elisa Mariarosaria
Bianco, Simone
Ciocca, Gianluigi
Remondino, Fabio
Publication Year :
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.

Details

Database :
OAIster
Notes :
STAMPA, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1376722291
Document Type :
Electronic Resource