@inproceedings{9616ef506b534c0a8ebca0cb4243efab,
title = "3D RECONSTRUCTION BY PARAMETERIZED SURFACE MAPPING",
abstract = "We introduce an approach for computing a 3D mesh from one or more views of an object by establishing dense correspondences between pixels in the views and 3D locations on a learnable parameterized surface. We propose a multi-view shape encoder that can be jointly trained with the AtlasNet surface parameterization. The shape is further refined using a novel geometric cycle-consistency loss between the learnable parameterized surface and input views. We demonstrate the efficacy of our approach on the ShapeNet-COCO dataset.",
keywords = "3D Reconstruction, Deformation, Learning, Multi-view, Surface mapping",
author = "Langlois, \{Pierre Alain\} and Matthew Fisher and Oliver Wang and Vladimir Kim and Alexandre Boulch and Renaud Marlet and Bryan Russell",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 28th IEEE International Conference on Image Processing, ICIP 2021 ; Conference date: 19-09-2021 Through 22-09-2021",
year = "2021",
month = jan,
day = "1",
doi = "10.1109/ICIP42928.2021.9506425",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "3273--3277",
booktitle = "2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings",
}