Prof. Dr. Didier Stricker
Dr. Alain Pagani
Dr. Gerd Reis
M.A. Eric Thil
Nicole Stephanie Buhlinger
Keonna Cunningham
Dr. Oliver Wasenmüller
Dr. Gabriele Bleser
Dr. Bertram Taetz
Sk Aziz Ali
Rashed Al Koutayni
Yuriy Anisimov
Jilliam Maria Diaz Barros
Ramy Battrawy
Hammad Butt
Mahdi Chamseddine
Steve Dias da Cruz
Fangwen Shu
Torben Fetzer
Michael Fürst
Christiano Couto Gava
Tewodros Amberbir Habtegebrial
Hartmut Feld
Jigyasa Katrolia
Andreas Kölsch
Onorina Kovalenko
Stephan Krauß
Paul Lesur
Muhammad Jameel Nawaz Malik
Mina Ameli
Nareg Minaskan Karabid
Pramod Murthy
Mathias Musahl
Peter Neigel
Manthan Pancholi
Jason Raphael Rambach
María Alejandra Sánchez Marín
Kripasindhu Sarkar
Alexander Schäfer
René Schuster
Mohamed Selim
Dennis Stumpf
Yongzhi Su
Xiaoying Tan
André Luiz Brandão
Ahmet Firintepe
Vladislav Golyanik
Aditya Tewari
3D Shape Processing by Convolutional Denoising Autoencoders on Local Patches
3D Shape Processing by Convolutional Denoising Autoencoders on Local Patches
IEEE Winter Conference on Applications of Computer Vision (WACV-18), March 12-15, Lake Tahoe, NV, USA
- Abstract:
- We propose a system for surface completion and inpainting of 3D shapes using denoising autoencoders with convolutional layers, learnt on local patches. Our method uses height map based local patches parameterized using 3D mesh quadrangulation of the low resolution input shape. This provides us sufficient amount of local 3D patch dataset to learn deep generative Convolutional Neural Networks (CNNs) for the task of repairing moderate sized holes. We design generative networks specifically suited for the 3D encoding following ideas from the recent progress in 2D inpainting, and show our results to be better than the previous methods of surface inpainting that use linear dictionary. We validate our method on both synthetic shapes and real world scans.
- Keywords:
- CNN, inpainting, shape processing, shape repair