D. Bojanic et al., "Evaluation of 3D Registration Deep Learning Methods using Iterative Transformation Estimations", Proc. of 3DBODY.TECH 2020 - 11th Int. Conf. and Exh. on 3D Body Scanning and Processing Technologies, Online/Virtual, 17-18 Nov. 2020, #31, https://doi.org/10.15221/20.31.

Title:

Evaluation of 3D Registration Deep Learning Methods using Iterative Transformation Estimations

Authors:

David BOJANIC 1, Kristijan BARTOL 1, Tomislav PETKOVIC 1, Nicola D'APUZZO 2, Tomislav PRIBANIC 1

1 University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia;
2 Hometrica Consulting, Ascona, Switzerland

Abstract:

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.

Keywords:

3d computer vision, 3d registration, deep learning

Details:

Full paper: 2031bojanic.pdf
Proceedings: 3DBODY.TECH 2020, 17-18 Nov. 2020, Online/Virtual
Paper id#: 31
DOI: 10.15221/20.31
Presentation video: 2031bojanic.mp4

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