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Erik Beeson

dblp:68/4979 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2007
—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 · 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.

Artificial intelligence
1 paper
3D vision · 50% Robot navigation and mapping · 50%
Computer graphics and multimedia
1 paper
Computational photography and imaging · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
depth estimation
0.112007
Viewpoint-Coded Structured Light · CVPR 2007
Robotics › Robot navigation and mapping › active perception › active depth estimation
structured light depth estimation
0.112007
Viewpoint-Coded Structured Light · CVPR 2007
Computational photography and imaging › depth estimation
depth reconstruction
0.112007
Viewpoint-Coded Structured Light · CVPR 2007
Computational photography and imaging › 3d scanning
structured light
0.112007
Viewpoint-Coded Structured Light · CVPR 2007

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

viewpoint coding · 0.1epipolar projection · 0.1binary encoding · 0.1
YearPublicationVenuePosition
2007 Viewpoint-Coded Structured Light
abstract
We introduce a theoretical framework and practical algorithms for replacing time-coded structured light patterns with viewpoint codes, in the form of additional camera locations. Current structured light methods typically use log(N) light patterns, encoded over time, to unambiguously reconstruct N unique depths. We demonstrate that each additional camera location may replace one frame in a temporal binary code. Our theoretical viewpoint coding analysis shows that, by using a high frequency stripe pattern and placing cameras in carefully selected locations, the epipolar projection in each camera can be made to mimic the binary encoding patterns normally projected over time. Results from our practical implementation demonstrate reliable depth reconstruction that makes neither temporal nor spatial continuity assumptions about the scene being captured.
Mark Young, Erik Beeson, James Davis 0001, Szymon Rusinkiewicz, Ravi Ramamoorthi
CVPR2