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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.11.20.09
%2 sid.inpe.br/sibgrapi/2021/09.11.20.09.14
%T Neural Networks for Implicit Representations of 3D Scenes
%D 2021
%A Schirmer, Luiz,
%A Schardong, Guilherme,
%A Silva, Vinícius da,
%A Novello, Tiago,
%A Yukimura, Daniel,
%A Magalhães, Thales,
%A Paz, Hallison,
%A Velho, Luiz,
%A Lopes, Hélio,
%@affiliation PUC-Rio
%@affiliation PUC-Rio
%@affiliation PUC-Rio
%@affiliation IMPA
%@affiliation IMPA
%@affiliation IMPA
%@affiliation IMPA
%@affiliation IMPA
%@affiliation PUC-Rio
%E Paiva, Afonso,
%E Menotti, David,
%E Baranoski, Gladimir V. G.,
%E Proença, Hugo Pedro,
%E Junior, Antonio Lopes Apolinario,
%E Papa, João Paulo,
%E Pagliosa, Paulo,
%E dos Santos, Thiago Oliveira,
%E e Sá, Asla Medeiros,
%E da Silveira, Thiago Lopes Trugillo,
%E Brazil, Emilio Vital,
%E Ponti, Moacir A.,
%E Fernandes, Leandro A. F.,
%E Avila, Sandra,
%B Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)
%C Gramado (Virtual), Brazil
%8 October 18th to October 22nd, 2021
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Neural Networks, Implicit Functions, Signal Distance Functions.
%X This survey presents methods that use neural networks for implicit representations of 3D geometry --- neural implicit functions. We explore the different aspects of neural implicit functions for shape modeling and synthesis. We aim to provide a theoretical analysis of 3D shape reconstruction using deep neural networks and introduce a discussion between researchers interested in this research field.
%@language en
%3 Tutorial_Sibgrapi_2021 (2).pdf


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