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10.1145/3415263.3419152acmconferencesArticle/Chapter ViewAbstractPublication Pagessiggraph-asiaConference Proceedingsconference-collections
Article

Learning 3D functionality representations

Published: 04 December 2020 Publication History

Abstract

A central goal of computer graphics is to provide tools for designing and simulating real or imagined artifacts. An understanding of functionality is important in enabling such modeling tools. Given that the majority of man-made artifacts are designed to serve a certain function, the functionality of objects is often reflected by their geometry, the way that they are organized in an environment, and their interaction with other objects or agents. Thus, in recent years, a variety of methods in shape analysis have been developed to extract functional information about objects and scenes from these different types of cues.
In this course, we discuss recent developments involving functionality analysis of 3D shapes and scenes. We provide a summary of the state-of-the-art in this area, including a discussion of key ideas and an organized review of the relevant literatures. More specifically, we first present a general definition of functionality from which we derive criteria for classifying the body of prior work. This definition facilitates a comparative view of methods for functionality analysis. Moreover, we connect these methods to recent advances in deep learning, computer vision and robotics. Finally, we discuss a variety of application areas, and outline current challenges and directions for future work.

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cover image ACM Conferences
SA '20: SIGGRAPH Asia 2020 Courses
November 2020
842 pages
ISBN:9781450381123
DOI:10.1145/3415263
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 04 December 2020

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Author Tags

  1. 3D representations
  2. deep learning
  3. functionality analysis
  4. geometric modeling
  5. shape analysis

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SA '20: SIGGRAPH Asia 2020
December 4 - 13, 2020
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Overall Acceptance Rate 178 of 869 submissions, 20%

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