Three-dimensional (3D) reconstruction from a photogrammetric perspective aims to infer the geometric structure of a scene from a set of images, including the recovery of depth information inherently lost during image acquisition. Conventional photogrammetric pipelines rely on multiple handcrafted processing stages, often requiring manual intervention. This work introduces a dataset designed to support the study of background removal techniques in photogrammetric workflows through salient object detection (SOD). The dataset comprises 15,120 images divided into sets of 28 distinct objects, each set including 36 high-resolution RGB images captured from multiple viewpoints. Additionally, each set provides 36 manually segmented images, as well as automatically segmented versions obtained using four different SOD algorithms. To facilitate evaluation and reproducibility, 153 reconstructed 3D models are provided across all object categories, and a 3D reconstruction evaluation methodology based on the Chamfer Distance metric is proposed, enabling the analysis of the impact of different segmentation strategies on 3D reconstruction. The dataset offers a benchmark resource for the development, comparison, and validation of methods aimed at improving photogrammetric pipelines through automated information filtering.
Román et al. (Thu,) studied this question.