Pregled bibliografske jedinice broj: 1093068
Evaluation of 3D Registration Deep Learning Methods using Iterative Transformation Estimations
Evaluation of 3D Registration Deep Learning Methods using Iterative Transformation Estimations // Proceedings of 3DBODY.TECH 2020 11th International Conference and Exhibition on 3D Body Scanning and Processing Technologies Online/Virtual, 17-18 November 2020 / D’Apuzzo , Nicola (ur.).
online: Hometrica Consulting - Dr. Nicola D'Apuzzo, 2020. 31, 10 doi:10.15221/20.31 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1093068 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Evaluation of 3D Registration Deep Learning
Methods using Iterative Transformation Estimations
Autori
Bojanić, David ; Bartol, Kristijan ; Petković, Tomislav ; D'Apuzzo, Nicola ; Pribanić, Tomislav
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of 3DBODY.TECH 2020 11th International Conference and Exhibition on 3D Body Scanning and Processing Technologies Online/Virtual, 17-18 November 2020
/ D’Apuzzo , Nicola - : Hometrica Consulting - Dr. Nicola D'Apuzzo, 2020
ISBN
978-3-033-08209-0
Skup
11th International Conference and Exhibition on 3D Body Scanning and Processing Technologies (3DBODY.TECH 2020)
Mjesto i datum
Online, 17.11.2020. - 18.11.2020
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
3d computer vision ; 3d registration ; deep learning
Sažetak
3D registration is a process of aligning multiple three-dimensional (3D) data structures (such as point clouds or meshes) and merging them into one consistent and seamless 3D data structure. With the scope of 3D reconstruction, 3D human body scans from multiple views need to be registered into a single point cloud to create a seamless 3D representation. Following current state-of-the-art deep learning approaches, we argue that an encoder-decoder approach, where the decoder part of the architecture uses a recursive layer that iteratively estimates the rigid transformation, should provide the best results. We adapt an approach created for the task of 3D segmentation called RSNets to the task of 3D registration and compare it to the current state-of- the-art algorithm PCRNet.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Projekti:
HRZZ-IP-2018-01-8118 - Izračun antropometrijskih mjera pametnim telefonom i tabletom (STEAM) (Pribanić, Tomislav, HRZZ ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Profili:
Tomislav Petković (autor)
Kristijan Bartol (autor)
Tomislav Pribanić (autor)
David Bojanić (autor)