Evaluation of 3D Registration Deep Learning Methods using Iterative Transformation Estimations (CROSBI ID 696652)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Bojanić, David ; Bartol, Kristijan ; Petković, Tomislav ; D'Apuzzo, Nicola ; Pribanić, Tomislav
engleski
Evaluation of 3D Registration Deep Learning Methods using Iterative Transformation Estimations
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.
3d computer vision ; 3d registration ; deep learning
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Podaci o prilogu
31
2020.
objavljeno
10.15221/20.31
Podaci o matičnoj publikaciji
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
978-3-033-08209-0
Podaci o skupu
11th International Conference and Exhibition on 3D Body Scanning and Processing Technologies (3DBODY.TECH 2020)
predavanje
17.11.2020-18.11.2020
online