Extracting Sharp Features from RGB‐D Images
Sharp edges are important shape features and their extraction has been extensively studied both on point clouds and surfaces. We consider the problem of extracting sharp edges from a sparse set of colour‐and‐depth (RGB‐D) images. The noise‐ridden depth measurements are challenging for existing featu...
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| Vydáno v: | Computer graphics forum Ročník 36; číslo 8; s. 138 - 152 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Oxford
Blackwell Publishing Ltd
01.12.2017
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Sharp edges are important shape features and their extraction has been extensively studied both on point clouds and surfaces. We consider the problem of extracting sharp edges from a sparse set of colour‐and‐depth (RGB‐D) images. The noise‐ridden depth measurements are challenging for existing feature extraction methods that work solely in the geometric domain (e.g. points or meshes). By utilizing both colour and depth information, we propose a novel feature extraction method that produces much cleaner and more coherent feature lines. We make two technical contributions. First, we show that intensity edges can augment the depth map to improve normal estimation and feature localization from a single RGB‐D image. Second, we designed a novel algorithm for consolidating feature points obtained from multiple RGB‐D images. By utilizing normals and ridge/valley types associated with the feature points, our algorithm is effective in suppressing noise without smearing nearby features.
Sharp edges are important shape features and their extraction has been extensively studied both on point clouds and surfaces. We consider the problem of extracting sharp edges from a sparse set of colour‐and‐depth (RGB‐D) images. The noise‐ridden depth measurements are challenging for existing feature extraction methods that work solely in the geometric domain (e.g. points or meshes). By utilizing both colour and depth information, we propose a novel feature extraction method that produces much cleaner and more coherent feature lines. We make two technical contributions. First, we show that intensity edges can augment the depth map to improve normal estimation and feature localization from a single RGB‐D image. Second, we designed a novel algorithm for consolidating feature points obtained from multiple RGB‐D images. By utilizing normals and ridge/valley types associated with the feature points, our algorithm is effective in suppressing noise without smearing nearby features. |
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| AbstractList | Sharp edges are important shape features and their extraction has been extensively studied both on point clouds and surfaces. We consider the problem of extracting sharp edges from a sparse set of colour‐and‐depth (RGB‐D) images. The noise‐ridden depth measurements are challenging for existing feature extraction methods that work solely in the geometric domain (e.g. points or meshes). By utilizing both colour and depth information, we propose a novel feature extraction method that produces much cleaner and more coherent feature lines. We make two technical contributions. First, we show that intensity edges can augment the depth map to improve normal estimation and feature localization from a single RGB‐D image. Second, we designed a novel algorithm for consolidating feature points obtained from multiple RGB‐D images. By utilizing normals and ridge/valley types associated with the feature points, our algorithm is effective in suppressing noise without smearing nearby features. Sharp edges are important shape features and their extraction has been extensively studied both on point clouds and surfaces. We consider the problem of extracting sharp edges from a sparse set of colour‐and‐depth (RGB‐D) images. The noise‐ridden depth measurements are challenging for existing feature extraction methods that work solely in the geometric domain (e.g. points or meshes). By utilizing both colour and depth information, we propose a novel feature extraction method that produces much cleaner and more coherent feature lines. We make two technical contributions. First, we show that intensity edges can augment the depth map to improve normal estimation and feature localization from a single RGB‐D image. Second, we designed a novel algorithm for consolidating feature points obtained from multiple RGB‐D images. By utilizing normals and ridge/valley types associated with the feature points, our algorithm is effective in suppressing noise without smearing nearby features. Sharp edges are important shape features and their extraction has been extensively studied both on point clouds and surfaces. We consider the problem of extracting sharp edges from a sparse set of colour‐and‐depth (RGB‐D) images. The noise‐ridden depth measurements are challenging for existing feature extraction methods that work solely in the geometric domain (e.g. points or meshes). By utilizing both colour and depth information, we propose a novel feature extraction method that produces much cleaner and more coherent feature lines. We make two technical contributions. First, we show that intensity edges can augment the depth map to improve normal estimation and feature localization from a single RGB‐D image. Second, we designed a novel algorithm for consolidating feature points obtained from multiple RGB‐D images. By utilizing normals and ridge/valley types associated with the feature points, our algorithm is effective in suppressing noise without smearing nearby features. |
| Author | Ju, T. Xu, J. Hu, S‐M. Cao, Y‐P. |
| Author_xml | – sequence: 1 givenname: Y‐P. surname: Cao fullname: Cao, Y‐P. email: caoyanpei@gmail.com organization: Department of Computer Science and Technology, Tsinghua University – sequence: 2 givenname: T. surname: Ju fullname: Ju, T. email: taoju@cse.wustl.edu organization: Washington University in St. Louis, St. Louis, Missouri – sequence: 3 givenname: J. surname: Xu fullname: Xu, J. email: eternal_answer@126.com organization: Department of Computer Science and Technology, Tsinghua University – sequence: 4 givenname: S‐M. surname: Hu fullname: Hu, S‐M. email: shimin@tsinghua.edu.cn organization: Department of Computer Science and Technology, Tsinghua University |
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| Copyright | 2016 The Authors Computer Graphics Forum © 2016 The Eurographics Association and John Wiley & Sons Ltd. 2017 The Eurographics Association and John Wiley & Sons Ltd. |
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| Snippet | Sharp edges are important shape features and their extraction has been extensively studied both on point clouds and surfaces. We consider the problem of... |
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| SubjectTerms | Color Consolidation Feature extraction Galling I.3.3 [Computer Graphics]: Picture/Image Generation — Digitizing and Scanning I.3.5 [Computer Graphics]: Computational Geometry and Object Modelling — Curve, surface, solid, and object representations modelling object scanning/acquisition point‐based graphics |
| Title | Extracting Sharp Features from RGB‐D Images |
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