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arXiv · 2308.10621

Multi-Modal Dataset Acquisition for Photometrically Challenging Object

HyunJun Jung·Patrick Ruhkamp·Nassir Navab·Benjamin Busam
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Abstract

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.

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HyunJun Jung, Patrick Ruhkamp, Nassir Navab, Benjamin Busam. 2023-08-21. Multi-Modal Dataset Acquisition for Photometrically Challenging Object. https://arxiv.org/abs/2308.10621

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