%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.11.20.09
%2 sid.inpe.br/sibgrapi/2021/09.11.20.09.14
%@doi 10.1109/SIBGRAPI54419.2021.00012
%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, RS, Brazil (virtual)
%8 18-22 Oct. 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