Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans

• URL: http://arxiv.org/abs/2112.09598v1
• Date: Fri, 17 Dec 2021 16:19:06 GMT
• Title: Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans
• Authors: Luk\'a\v{s} Gajdo\v{s}ech, Viktor Kocur, Martin Stuchl\'ik, Luk\'a\v{s} Hudec, Martin Madaras
• Abstract summary: This paper focuses on a specific task of 6D pose estimation of a bin in 3D scans. We present a high-quality dataset composed of synthetic data and real scans captured by a structured-light scanner with precise annotations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: An automated robotic system needs to be as robust as possible and fail-safe in general while having relatively high precision and repeatability. Although deep learning-based methods are becoming research standard on how to approach 3D scan and image processing tasks, the industry standard for processing this data is still analytically-based. Our paper claims that analytical methods are less robust and harder for testing, updating, and maintaining. This paper focuses on a specific task of 6D pose estimation of a bin in 3D scans. Therefore, we present a high-quality dataset composed of synthetic data and real scans captured by a structured-light scanner with precise annotations. Additionally, we propose two different methods for 6D bin pose estimation, an analytical method as the industrial standard and a baseline data-driven method. Both approaches are cross-evaluated, and our experiments show that augmenting the training on real scans with synthetic data improves our proposed data-driven neural model. This position paper is preliminary, as proposed methods are trained and evaluated on a relatively small initial dataset which we plan to extend in the future.

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