Quality-driven poisson-guided autoscanning
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Published:2014-11-19
Issue:6
Volume:33
Page:1-12
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ISSN:0730-0301
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Container-title:ACM Transactions on Graphics
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language:en
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Short-container-title:ACM Trans. Graph.
Author:
Wu Shihao1,
Sun Wei1,
Long Pinxin1,
Huang Hui1,
Cohen-Or Daniel2,
Gong Minglun3,
Deussen Oliver4,
Chen Baoquan5
Affiliation:
1. Shenzhen VisuCA Key Lab/SIAT
2. Tel-Aviv University
3. Memorial University of Newfoundland
4. University of Konstanz
5. Shandong University
Abstract
We present a quality-driven, Poisson-guided autonomous scanning method. Unlike previous scan planning techniques, we do not aim to minimize the number of scans needed to cover the object's surface, but rather to ensure the high quality scanning of the model. This goal is achieved by placing the scanner at strategically selected Next-Best-Views (NBVs) to ensure progressively capturing the geometric details of the object, until both completeness and high fidelity are reached. The technique is based on the analysis of a Poisson field and its geometric relation with an input scan. We generate a confidence map that reflects the quality/fidelity of the estimated Poisson iso-surface. The confidence map guides the generation of a viewing vector field, which is then used for computing a set of NBVs. We applied the algorithm on two different robotic platforms, a PR2 mobile robot and a one-arm industry robot. We demonstrated the advantages of our method through a number of autonomous high quality scannings of complex physical objects, as well as performance comparisons against state-of-the-art methods.
Funder
Natural Sciences and Engineering Research Council of Canada
Israel Science Foundation
Ministry of Science and Technology of the People's Republic of China
Shenzhen Technology Innovation Program
National Natural Science Foundation of China
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Graphics and Computer-Aided Design
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