research-article

How well do line drawings depict shape?

Authors:

Forrester Cole,

Adam Finkelstein,

Thomas Funkhouser,

Szymon Rusinkiewicz,

Manish SinghAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 28, Issue 3

Article No.: 28, Pages 1 - 9

https://doi.org/10.1145/1531326.1531334

Published: 27 July 2009 Publication History

Abstract

This paper investigates the ability of sparse line drawings to depict 3D shape. We perform a study in which people are shown an image of one of twelve 3D objects depicted with one of six styles and asked to orient a gauge to coincide with the surface normal at many positions on the object's surface. The normal estimates are compared with each other and with ground truth data provided by a registered 3D surface model to analyze accuracy and precision. The paper describes the design decisions made in collecting a large data set (275,000 gauge measurements) and provides analysis to answer questions about how well people interpret shapes from drawings. Our findings suggest that people interpret certain shapes almost as well from a line drawing as from a shaded image, that current computer graphics line drawing techniques can effectively depict shape and even match the effectiveness of artist's drawings, and that errors in depiction are often localized and can be traced to particular properties of the lines used. The data collected for this study will become a publicly available resource for further studies of this type.

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Cited By

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Index Terms

How well do line drawings depict shape?
1. Computing methodologies
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Computational geometry

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 28, Issue 3

August 2009

750 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1531326

Issue’s Table of Contents

Copyright © 2009 ACM.

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Association for Computing Machinery

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Publication History

Published: 27 July 2009

Published in TOG Volume 28, Issue 3

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Hertzmann A(2024)Generative Models for the Psychology of Art and AestheticsEmpirical Studies of the Arts10.1177/02762374241288696Online publication date: 7-Oct-2024
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Nguyen VFisher MHertzmann ARusinkiewicz S(2024)Region‐Aware Simplification and Stylization of 3D Line DrawingsComputer Graphics Forum10.1111/cgf.1504243:2Online publication date: 24-Apr-2024
https://doi.org/10.1111/cgf.15042
Hertzmann A(2024)New Insights in Smooth Occluding Contours for Nonphotorealistic RenderingIEEE Computer Graphics and Applications10.1109/MCG.2023.333878444:1(76-85)Online publication date: Jan-2024
https://doi.org/10.1109/MCG.2023.3338784
Sawada TVolk D(2024)An accidental image feature that appears but not disappearsJournal of Mathematical Psychology10.1016/j.jmp.2024.102841119(102841)Online publication date: Apr-2024
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Liu TYang ZHu SZhang ZXiao CGuo XYang L(2023)Neighbor Reweighted Local Centroid for Geometric Feature IdentificationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.312491129:2(1545-1558)Online publication date: 1-Feb-2023
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Preim BRaidou RSmit NLawonn K(2023)3D visualization for anatomy educationVisualization, Visual Analytics and Virtual Reality in Medicine10.1016/B978-0-12-822962-0.00015-8(159-197)Online publication date: 2023
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Sawada TMendoza Arvizu AFarshchi MKiba A(2022)Navigation in Contour-Drawn Scenes Using Augmented Realityi-Perception10.1177/2041669522107470713:1Online publication date: 31-Jan-2022
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