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Yuichi Ota

dblp:58/9054 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
—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.

Computer graphics and multimedia
1 paper
Geometric modeling and processing · 50% Computational photography and imaging · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing
3d reconstruction
0.112009
Dense 3D reconstruction method using a single pattern for fast moving object · ICCV 2009
Computational photography and imaging › 3d scanning
structured light scanning
0.112009
Dense 3D reconstruction method using a single pattern for fast moving object · ICCV 2009

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

shape from intersections of lines · 0.1belief propagation · 0.1
YearPublicationVenuePosition
2009 Dense 3D reconstruction method using a single pattern for fast moving object
abstract
Dense 3D reconstruction of extremely fast moving objects could contribute to various applications such as body structure analysis and accident avoidance and so on. The actual cases for scanning we assume are, for example, acquiring sequential shape at the moment when an object explodes, or observing fast rotating turbine's blades. In this paper, we propose such a technique based on a one-shot scanning method that reconstructs 3D shape from a single image where dense and simple pattern are projected onto an object. To realize dense 3D reconstruction from a single image, there are several issues to be solved; e.g. instability derived from using multiple colors, and difficulty on detecting dense pattern because of influence of object color and texture compression. This paper describes the solutions of the issues by combining two methods, that is (1) an efficient line detection technique based on de Bruijn sequence and belief propagation, and (2) an extension of shape from intersections of lines method. As a result, a scanning system that can capture an object in fast motion has been actually developed by using a high-speed camera. In the experiments, the proposed method successfully captured the sequence of dense shapes of an exploding balloon, and a breaking ceramic dish at 300–1000 fps.
Ryusuke Sagawa, Yuichi Ota, Yasushi Yagi, Ryo Furukawa 0001, Naoki Asada, Hiroshi Kawasaki
ICCV2