Multi-Modal Dataset Acquisition for Photometrically Challenging Object

• URL: http://arxiv.org/abs/2308.10621v1
• Date: Mon, 21 Aug 2023 10:38:32 GMT
• Title: Multi-Modal Dataset Acquisition for Photometrically Challenging Object
• Authors: HyunJun Jung, Patrick Ruhkamp, Nassir Navab, Benjamin Busam
• Abstract summary: 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.
• Score: 56.30027922063559
• License: http://creativecommons.org/licenses/by/4.0/
• 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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