short-paper

Context-aware Geometric Object Reconstruction for Mobile Education

Authors:

Zhi TangAuthors Info & Claims

MM '16: Proceedings of the 24th ACM international conference on Multimedia

Pages 367 - 371

https://doi.org/10.1145/2964284.2967244

Published: 01 October 2016 Publication History

Abstract

The solid geometric objects in the educational geometric books are usually illustrated as 2D line drawings accompanied with description text. In this paper, we present a method to recover the geometric objects from 2D to 3D. Unlike the previous methods, we not only use the geometric information from the line drawing itself, but also the textual information extracted from its context. The essential of our method is a cost function to mix the two types of information, and we optimize the cost function to identify the geometric object and recover its 3D information. Our method can recover various types of solid geometric objects including straight-edge manifolds and curved objects such as cone, cylinder and sphere. We show that our method performs significantly better compared to the previous ones.

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Cited By

Wang ZFang QWang Y(2021)Geometric Object 3D Reconstruction from Single Line Drawing Image Based on a Network for Classification and Sketch ExtractionDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86549-8_38(598-613)Online publication date: 2-Sep-2021
https://doi.org/10.1007/978-3-030-86549-8_38
Guo TCui RQin XWang YTang ZBao HIp HSeidel HSheffer AFu HGhosh AKopf J(2018)Bottom-up/top-down geometric object reconstruction with CNN classification for mobile educationProceedings of the 26th Pacific Conference on Computer Graphics and Applications: Short Papers10.2312/pg.20181269(13-16)Online publication date: 8-Oct-2018
https://dl.acm.org/doi/10.2312/pg.20181269

Index Terms

Context-aware Geometric Object Reconstruction for Mobile Education
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '16: Proceedings of the 24th ACM international conference on Multimedia

October 2016

1542 pages

ISBN:9781450336031

DOI:10.1145/2964284

General Chairs:
Alan Hanjalic
Delft University of Technology
,
Cees Snoek
Qualcomm Research Netherlands / University of Amsterdam
,
Marcel Worring
University of Amsterdam
,
Moderator:
Dick Bulterman
CWI / VU University Amsterdam
,
Program Chairs:
Benoit Huet
EURECOM
,
Aisling Kelliher
Virginia Tech
,
Yiannis Kompatsiaris
CERTH-ITI
,
Jin Li
Microsoft

Copyright © 2016 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2016

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National Natural Science Foundation of China

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MM '16

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SIGMM

MM '16: ACM Multimedia Conference

October 15 - 19, 2016

Amsterdam, The Netherlands

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MM '16 Paper Acceptance Rate 52 of 237 submissions, 22%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Wang ZFang QWang Y(2021)Geometric Object 3D Reconstruction from Single Line Drawing Image Based on a Network for Classification and Sketch ExtractionDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86549-8_38(598-613)Online publication date: 2-Sep-2021
https://doi.org/10.1007/978-3-030-86549-8_38
Guo TCui RQin XWang YTang ZBao HIp HSeidel HSheffer AFu HGhosh AKopf J(2018)Bottom-up/top-down geometric object reconstruction with CNN classification for mobile educationProceedings of the 26th Pacific Conference on Computer Graphics and Applications: Short Papers10.2312/pg.20181269(13-16)Online publication date: 8-Oct-2018
https://dl.acm.org/doi/10.2312/pg.20181269

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