Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi !

On using PointNet Architecture for Human Body Segmentation (CROSBI ID 682520)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Jertec, Andrej ; Bojanić, David ; Bartol, Kristijan ; Pribanić, Tomislav ; Petković, Tomislav ; Petrak , Slavenka On using PointNet Architecture for Human Body Segmentation // 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) / Lončarić, Sven ; Bregović, Robert ; Carli, Marco et al. (ur.). Dubrovnik: Institute of Electrical and Electronics Engineers (IEEE), 2019. str. 253-257 doi: 10.1109/ISPA.2019.8868844

Podaci o odgovornosti

Jertec, Andrej ; Bojanić, David ; Bartol, Kristijan ; Pribanić, Tomislav ; Petković, Tomislav ; Petrak , Slavenka

engleski

On using PointNet Architecture for Human Body Segmentation

In the case of structured data, such as 2D images, many variants of traditional convolution neural network architectures have been successfully proposed. Learning from unstructured sets of data, such as sets of 3D point clouds, is a challenging task due to numerous reasons among which two most important ones are: 3D point cloud is generally (i) unordered and (ii) sparse data set. Therefore, the architectures have been proposed which are invariant to both ordering and number of points in the point cloud. PointNet is one such architecture, originally introduced and demonstrated on the task of classification and segmentation of the ModelNet40 data set. In this work we study the performance of PointNet on an even more demanding task, segmentation of human body parts. Finding enough training data of enough quality is generally a problem in deep learning, and especially for human body segmentation. To that end we take advantage of SMPL model which provides human body models in many shapes and sizes in an essentially automatic fashion, therefore avoiding a cumbersome procedure of manual collection and preparation of training data. Our results show that the proposed PointNet variant trained using SMPL model provides competitive segmentation results on the task of human body segmentation.

PointNet, human body segmentation, 3D shape analysis, deep learning

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

Podaci o prilogu

253-257.

2019.

objavljeno

10.1109/ISPA.2019.8868844

Podaci o matičnoj publikaciji

2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA)

Lončarić, Sven ; Bregović, Robert ; Carli, Marco ; Subašić, Marko

Dubrovnik: Institute of Electrical and Electronics Engineers (IEEE)

978-1-7281-3140-5

1849-2266

Podaci o skupu

11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019)

predavanje

23.09.2019-25.09.2019

Dubrovnik, Hrvatska

Povezanost rada

Računarstvo, Interdisciplinarne tehničke znanosti

Poveznice