course

Data-driven shape analysis and processing

Authors:

Vladimir G. Kim,

Evangelos KalogerakisAuthors Info & Claims

SA '16: SIGGRAPH ASIA 2016 Courses

Article No.: 4, Pages 1 - 38

https://doi.org/10.1145/2988458.2988473

Published: 28 November 2016 Publication History

Abstract

Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.

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Index Terms

Data-driven shape analysis and processing
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        Computational photography
  2. Computer graphics
    1. Image manipulation

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ISBN:9781450345385

DOI:10.1145/2988458

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University College London, United Kingdom

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https://doi.org/10.1061/JAEEEZ.ASENG-5333
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https://dl.acm.org/doi/10.1145/3705013
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https://doi.org/10.1109/ICRA48891.2023.10160927
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Lin CFang LLi SLuo BLin J(2022)Hybrid Artificial Immune System for Controlled Procedural Modeling2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)10.1109/ICPECA53709.2022.9719153(1197-1206)Online publication date: 21-Jan-2022
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Zhou PZhu JHuo SNavarro-Alarcon D(2021)LaSeSOM: A Latent and Semantic Representation Framework for Soft Object ManipulationIEEE Robotics and Automation Letters10.1109/LRA.2021.30748726:3(5381-5388)Online publication date: Jul-2021
https://doi.org/10.1109/LRA.2021.3074872
Huang QHuang XSun BZhang ZJiang JBajaj C(2021)ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators2021 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV48922.2021.00576(5795-5805)Online publication date: Oct-2021
https://doi.org/10.1109/ICCV48922.2021.00576
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