Journal ArticleDOI
Revealing Hidden 3-D Reflection Symmetry
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TLDR
This letter proposes an energy minimizationbased approach to detect the reflection symmetry present in the object from its multiple 2-D projections captured from different viewpoints and the sparse 3-D model obtained using these projections.Citations
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3DSymm: Robust and Accurate 3D Reflection Symmetry Detection
TL;DR: This work proposes a descriptor-free approach, in which, the problem of reflection symmetry detection as an optimization problem and provide a closed-form solution, and shows that the proposed method achieves state-of-the-art performance on the standard dataset.
Journal ArticleDOI
Reflection Symmetry Axes Detection Using Multiple Model Fitting
TL;DR: A novel $k-symmetry clustering algorithm is proposed to minimize this energy function in order to efficiently find all the symmetry axes present in the given image.
Proceedings ArticleDOI
An improved point cloud registration three-stage (3S) method using RGB-D camera
TL;DR: Results from the conducted experiments show improvement in point cloud registration quality (reduction in visual odometry and loop closure problem) when compared with three other registration methods.
References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Book
Multiple view geometry in computer vision
Richard Hartley,Andrew Zisserman +1 more
TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Multiple View Geometry in Computer Vision.
TL;DR: This book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts and it will show the best book collections and completed collections.
Related Papers (5)
Exploiting Symmetry and/or Manhattan Properties for 3D Object Structure Estimation from Single and Multiple Images
Yuan Gao,Alan L. Yuille +1 more