@inproceedings{e392ab4a6fbb40b9adf01a6b46562f7a,
title = "Deep meta functionals for shape representation",
abstract = "We present a new method for 3D shape reconstruction from a single image, in which a deep neural network directly maps an image to a vector of network weights. The network parametrized by these weights represents a 3D shape by classifying every point in the volume as either within or outside the shape. The new representation has virtually unlimited capacity and resolution, and can have an arbitrary topology. Our experiments show that it leads to more accurate shape inference from a 2D projection than the existing methods, including voxel-, silhouette-, and mesh-based methods. The code will be available at: Https: //github.com/gidilittwin/Deep-Meta.",
author = "Gidi Littwin and Lior Wolf",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 ; Conference date: 27-10-2019 Through 02-11-2019",
year = "2019",
month = oct,
doi = "10.1109/ICCV.2019.00191",
language = "الإنجليزيّة",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1824--1833",
booktitle = "Proceedings - 2019 International Conference on Computer Vision, ICCV 2019",
address = "الولايات المتّحدة",
}