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Kevin Sanik

dblp:25/7188 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Rendering · 87% Visualization and visual analytics · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Rendering › non-photorealistic rendering
line drawing
0.112009
How well do line drawings depict shape? · ACM Trans. Graph. 2009
Rendering › non-photorealistic rendering
shape depiction
0.112009
How well do line drawings depict shape? · ACM Trans. Graph. 2009
Visualization and visual analytics › perception
perceptual studies
0.012009
How well do line drawings depict shape? · ACM Trans. Graph. 2009

Methods — techniques the papers use, named apart from their topics

user study · 0.1surface normal estimation · 0.1gauge measurement · 0.1
YearPublicationVenuePosition
2011 Perception of intentions and mental states in autonomous virtual agents
Peter C. Pantelis, Steven A. Cholewiak, Paul Ringstad, Kevin Sanik, Ari Weinstein, Chia-Chien Wu, Jacob Feldman
CogSci4
2009 How well do line drawings depict shape?
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.
Forrester Cole, Kevin Sanik, Douglas DeCarlo, Adam Finkelstein, Thomas A. Funkhouser, Szymon Rusinkiewicz, Manish Singh 0001
ACM Trans. Graph.2