Extracting Sharp Features from RGB‐D Images
| dc.contributor.author | Cao, Y‐P. | en_US |
| dc.contributor.author | Ju, T. | en_US |
| dc.contributor.author | Xu, J. | en_US |
| dc.contributor.author | Hu, S‐M. | en_US |
| dc.contributor.editor | Chen, Min and Zhang, Hao (Richard) | en_US |
| dc.date.accessioned | 2018-01-10T07:42:44Z | |
| dc.date.available | 2018-01-10T07:42:44Z | |
| dc.date.issued | 2017 | |
| dc.description.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. | en_US |
| dc.description.number | 8 | |
| dc.description.sectionheaders | Articles | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 36 | |
| dc.identifier.doi | 10.1111/cgf.13069 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 138-152 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13069 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13069 | |
| dc.publisher | © 2017 The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | point‐based graphics | |
| dc.subject | modelling | |
| dc.subject | object scanning/acquisition | |
| dc.subject | 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 | |
| dc.title | Extracting Sharp Features from RGB‐D Images | en_US |