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Yi-Chin Wu

dblp:37/5318 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2019
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-authorApplied, 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.

Artificial intelligence
1 paper
3D vision · 100%
Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%
Human-computer interaction and pervasive computing
1 paper
Personal fabrication and tangible interfaces · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
3d reconstruction
0.412019
Tangible and Visible 3D Object Reconstruction in Augmented Reality · ISMAR 2019
Virtual and augmented reality › augmented reality
augmented reality assistance
0.412019
Tangible and Visible 3D Object Reconstruction in Augmented Reality · ISMAR 2019
Personal fabrication and tangible interfaces › computational design tools
3d modeling
0.112019
Tangible and Visible 3D Object Reconstruction in Augmented Reality · ISMAR 2019

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

user study · 1.1RGB-D camera · 1.1
YearPublicationVenuePosition
2019 Tangible and Visible 3D Object Reconstruction in Augmented Reality
abstract
Many crucial applications in the fields of filmmaking, game design, education, and cultural preservation - among others - involve the modeling, authoring, or editing of 3D objects and scenes. The two major methods of creating 3D models are 1) modeling, using computer software, and 2) reconstruction, generally using high-quality 3D scanners. Scanners of sufficient quality to support the latter method remain unaffordable to the general public. Since the emergence of consumer-grade RGBD cameras, there has been a growing interest in 3D reconstruction systems using depth cameras. However, most such systems are not user-friendly, and require intense efforts and practice if good reconstruction results are to be obtained. In this paper, we propose to increase the accessibility of depth-camera-based 3D reconstruction by assisting its users with augmented reality (AR) technology. Specifically, the proposed approach will allow users to rotate/move a target object freely with their hands and see the object being overlapped with its reconstructing model during the reconstruction process. As well as being more instinctual than conventional reconstruction systems, our proposed system will provide useful hints on complete 3D reconstruction of an object, including the best capturing range; reminder of moving and rotating the object at a steady speed; and which model regions are complex enough to require zooming-in. We evaluated our system via a user study that compared its performance against those of three other stateof- the-art approaches, and found our system outperforms the other approaches. Specifically, the participants rated it highest in usability, understandability, and model satisfaction.
Yi-Chin Wu, Li-Wei Chan 0001, Wen-Chieh Lin
ISMAR1
2018 Synthesis of Obfuscation Policies to Ensure Privacy and Utility
Yi-Chin Wu, Vasumathi Raman, Blake C. Rawlings, Stéphane Lafortune, Sanjit A. Seshia
J. Autom. Reason.1
2007 Rethinking Online Faculty Instructional Development: A Case Study
Shinn-Rong Lin, Yi-Chin Wu, Chia-Wen Liu, Hsiao-Ting Teng
ICCE2