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Shape-From-Template with Curves

delete2019-09-04
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PRE
AI
M
Mathias Gallardo *
D
Daniel Pizarro
T
Toby Collins
A
Adrien Bartoli
DOI:10.1007/s11263-019-01214-zdelete
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摘要

摘要

En 中文
Shape-from-Template (SfT) is the problem of using a shape template to infer the shape of a deformable object observed in an image. The usual case of SfT is 'Surface' SfT, where the shape is a 2D surface embedded in 3D, and the image is a 2D perspective projection. We introduce 'Curve' SfT, comprising two new cases of SfT where the shape is a 1D curve. The first new case is when the curve is embedded in 2D and the image a 1D perspective projection. The second new case is when the curve is embedded in 3D and the image a 2D perspective projection. We present a thorough theoretical study of these new cases for isometric deformations, which are a good approximation of ropes, cables and wires. Unlike Surface SfT, we show that Curve SfT is only ever solvable up to discrete ambiguities. We present the necessary and sufficient conditions for solvability with critical point analysis. We further show that unlike Surface SfT, Curve SfT cannot be solved locally using exact non-holonomic Partial Differential Equations. Our main technical contributions are two-fold. First, we give a stable, global reconstruction method that models the problem as a discrete Hidden Markov Model. This can generate all candidate solutions. Second, we give a non-convex refinement method using a novel angle-based deformation parameterization. We present quantitative and qualitative results showing that real curve shaped objects such as a necklace can be successfully reconstructed with Curve SfT.
Keyword:
Curve reconstruction
Shape-from-Template
Isometry
Discrete Hidden Markov Model
Partial differential equations
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AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

International Journal of Computer Vision 封面图
International Journal of Computer Vision
IF:
9.3
论文数:
3.9K
被引数:
2.8W

机构

C
centre national de la recherche scientifique (cnrs)
学者数:
24.5W
论文数: 18.2W
被引数: 279
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