This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging objects. We propose a novel annotation and acquisition pipeline that enhances existing 3D perception and 6D object pose datasets. Our approach integrates robotic forward-kinematics, external infrared trackers, and improved calibration and annotation procedures. We present a multi-modal sensor rig, mounted on a robotic end-effector, and demonstrate how it is integrated into the creation of highly accurate datasets. Additionally, we introduce a freehand procedure for wider viewpoint coverage. Both approaches yield high-quality 3D data with accurate object and camera pose annotations. Our methods overcome the limitations of existing datasets and provide valuable resources for 3D vision research.
@article{arxiv.2308.10621,
title = {Multi-Modal Dataset Acquisition for Photometrically Challenging Object},
author = {HyunJun Jung and Patrick Ruhkamp and Nassir Navab and Benjamin Busam},
journal= {arXiv preprint arXiv:2308.10621},
year = {2023}
}