Pregled bibliografske jedinice broj: 1028057
On using PointNet Architecture for Human Body Segmentation
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 ; Subašić, Marko (ur.).
Dubrovnik: Institute of Electrical and Electronics Engineers (IEEE), 2019. str. 253-257 doi:10.1109/ISPA.2019.8868844 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1028057 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
On using PointNet Architecture for Human Body Segmentation
Autori
Jertec, Andrej ; Bojanić, David ; Bartol, Kristijan ; Pribanić, Tomislav ; Petković, Tomislav ; Petrak , Slavenka
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
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), 2019, 253-257
ISBN
978-1-7281-3140-5
Skup
11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019)
Mjesto i datum
Dubrovnik, Hrvatska, 23.09.2019. - 25.09.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
PointNet, human body segmentation, 3D shape analysis, deep learning
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti
POVEZANOST RADA
Projekti:
KK.01.1.1.01.0009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (EK )
HRZZ-IP-2018-01-8118 - Izračun antropometrijskih mjera pametnim telefonom i tabletom (STEAM) (Pribanić, Tomislav, HRZZ ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Tekstilno-tehnološki fakultet, Zagreb
Profili:
Tomislav Petković (autor)
Kristijan Bartol (autor)
Slavenka Petrak (autor)
Tomislav Pribanić (autor)
David Bojanić (autor)