  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.
