Abstract

This dataset contains experimental data related to object 3D shape reconstruction from multiple viewpoints, produced in the framework of REMODEL project. Self-aware robots rely on depth sensing to interact with the surrounding environment, e.g. to pursue object grasping. Yet, dealing with tiny items, often occurring in industrial robotics scenarios, may represent a challenge due to lack of sensors yielding sufficiently accurate depth measurements. Existing active sensors fail at measuring details of small objects (<; 1cm) because of limitations in the working range, e.g. usually beyond 50 cm away, while off-the-shelf stereo cameras are not suited to close-range acquisitions due to the need for extremely short baselines. Therefore, we propose a framework designed for accurate depth sensing and particularly amenable to reconstruction of miniature objects. By leveraging on a single camera mounted in eye-on-hand configuration and the high repeatability of a robot, we acquire multiple images and process them through a stereo algorithm revised to fully exploit multiple vantage points. This dataset addresses performance evaluation in industrial applications using Single camera Stereo Robot (SiSteR), which delivers high accuracy even when dealing with miniature objects. The data are presented in the publication: D. De Gregorio, M. Poggi, P. Z. Ramirez, G. Palli, S. Mattoccia and L. Di Stefano, "Beyond the Baseline: 3D Reconstruction of Tiny Objects With Single Camera Stereo Robot," in IEEE Access, vol. 9, pp. 119755-119765, 2021, doi: 10.1109/ACCESS.2021.3108626.