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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU147766" target="_blank" >RIV/00216305:26230/22:PU147766 - isvavai.cz</a>

Result on the web

<a href="https://www.scitepress.org/Link.aspx?doi=10.5220/0010878200003124" target="_blank" >https://www.scitepress.org/Link.aspx?doi=10.5220/0010878200003124</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0010878200003124" target="_blank" >10.5220/0010878200003124</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP

ISBN

978-989-758-555-5

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

545-552

Publisher name

SciTePress - Science and Technology Publications

Place of publication

Setubal

Event location

Online

Event date

Feb 6, 2022

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

000777569400058