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An automated robotic system needs to be as robust as possible and fail-safein general while having relatively high precision and repeatability. Althoughdeep learning-based methods are becoming research standard on how to approach3D scan and image processing tasks, the industry standard for processing thisdata is still analytically-based. Our paper claims that analytical methods areless robust and harder for testing, updating, and maintaining. This paperfocuses 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 andreal scans captured by a structured-light scanner with precise annotations.Additionally, we propose two different methods for 6D bin pose estimation, ananalytical method as the industrial standard and a baseline data-driven method.Both approaches are cross-evaluated, and our experiments show that augmentingthe training on real scans with synthetic data improves our proposeddata-driven neural model. This position paper is preliminary, as proposedmethods are trained and evaluated on a relatively small initial dataset whichwe plan to extend in the future.

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