Classification of Hanging Garments Using Learned Features Extracted from 3D Point Clouds

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F18%3A00323132" target="_blank" >RIV/68407700:21730/18:00323132 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8593741" target="_blank" >https://ieeexplore.ieee.org/document/8593741</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IROS.2018.8593741" target="_blank" >10.1109/IROS.2018.8593741</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of Hanging Garments Using Learned Features Extracted from 3D Point Clouds

Popis výsledku v původním jazyce

The presented work deals with classification of garment categories including pants, shorts, shirts, T-shirts and towels. The knowledge of the garment category is crucial for its robotic manipulation. Our work focuses particularly on garments being held in a hanging state by a robotic arm. The input of our method is a set of depth maps taken from different viewpoints around the garment. The depths are fused into a single 3D point cloud. The cloud is fed into a convolutional neural network that transforms it into a single global feature vector. The network utilizes a generalized convolution operation defined over the local neighborhood of a point. It can deal with permutations of the input points. It was trained on a large dataset of common 3D objects. The extracted feature vector is classified with SVM trained on smaller datasets of garments. The proposed method was evaluated on publicly available data and compared to the original methods, achieving competitive performance and better generalization capability.

Název v anglickém jazyce

Classification of Hanging Garments Using Learned Features Extracted from 3D Point Clouds

Popis výsledku anglicky

The presented work deals with classification of garment categories including pants, shorts, shirts, T-shirts and towels. The knowledge of the garment category is crucial for its robotic manipulation. Our work focuses particularly on garments being held in a hanging state by a robotic arm. The input of our method is a set of depth maps taken from different viewpoints around the garment. The depths are fused into a single 3D point cloud. The cloud is fed into a convolutional neural network that transforms it into a single global feature vector. The network utilizes a generalized convolution operation defined over the local neighborhood of a point. It can deal with permutations of the input points. It was trained on a large dataset of common 3D objects. The extracted feature vector is classified with SVM trained on smaller datasets of garments. The proposed method was evaluated on publicly available data and compared to the original methods, achieving competitive performance and better generalization capability.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

ISBN

978-1-5386-8095-7

ISSN

2153-0858

e-ISSN

2153-0866

Počet stran výsledku

6

Strana od-do

5307-5312

Název nakladatele

IEEE Press

Místo vydání

New York

Místo konání akce

Madrid

Datum konání akce

1. 10. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000458872704125