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

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.

Automated summary

High-resolution data and accurate ground truth are crucial for evaluating and comparing methods and algorithms effectively. However, acquiring real data that is representative and diverse in a given application domain is often challenging. To address this issue, this paper introduces a new synthetic dataset called ENRICH for testing photogrammetric and computer vision algorithms. Compared to existing datasets, ENRICH provides higher resolution images with various lighting conditions, camera orientations, scales, and fields of view. ENRICH consists of three sub-datasets: ENRICH-Aerial, ENRICH-Square, and ENRICH-Statue, each showcasing different characteristics. The usefulness of this dataset is demonstrated through various photogrammetry and computer vision tasks, such as evaluating hand-crafted and deep learning-based features, examining the effects of ground control points (GCPs) configuration on 3D accuracy, and monocular depth estimation. ENRICH is publicly available at: https://github.com/davidemarelli/ENRICH.