Extracting Sharp Features from RGB-D Images

Yuan-Pei Cao Tsinghua University Tao Ju Washington University St Louis Jie XU MIT Shi-Min Hu Tsinghua University

Geometric Modeling and Processing mathscidoc:1808.16002

Computer Graphics Forum, 35, (8), 138-174, 2017
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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  title={Extracting Sharp Features from RGB-D Images },
  author={Yuan-Pei Cao, Tao Ju, Jie XU, and Shi-Min Hu},
  booktitle={Computer Graphics Forum},
Yuan-Pei Cao, Tao Ju, Jie XU, and Shi-Min Hu. Extracting Sharp Features from RGB-D Images . 2017. Vol. 35. In Computer Graphics Forum. pp.138-174. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20180817222023939033147.
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