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| Format: | Artículo científico |
| Language: | en |
| Published: |
Universidad Distrital Francisco José de Caldas
2016
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| Online Access: | https://www.redalyc.org/articulo.oa?id=498853954005 https://www.redalyc.org/journal/4988/498853954005/ https://www.redalyc.org/journal/4988/498853954005/html/ https://www.redalyc.org/journal/4988/498853954005/498853954005.epub https://www.redalyc.org/journal/4988/498853954005/movil |
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| _version_ | 1866812984503828480 |
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| author | Angie Quijano |
| author_facet | Angie Quijano |
| contents | 3D Semantic Modeling of Indoor Environments based on Point Clouds and Contextual Relationships Angie Quijano Flavio Prieto Ingeniería Kinect point cloud semantic modeling Indoor environment Context: We propose a methodology to identify and label the components of a typical indoor environment in order to generate a semantic model of the scene. We are interested in identifying walls, ceilings, floors, doorways with open doors, doorways with closed doors that are recessed into walls, and partially occluded windows. Method: The elements to be identified should be flat in case of walls, floors, and ceilings and should have a rectangular shape in case of windows and doorways, which means that the indoor structure is Manhattan. The identification of these structures is determined through the analysis of the contextual relationships among them as parallelism, orthogonality, and position of the structure in the scene. Point clouds were acquired using a RGB-D device (Microsoft Kinect Sensor). Results: The obtained results show a precision of 99.03% and a recall of 95.68%, in a proprietary dataset. Conclusions: A method for 3D semantic labeling of indoor scenes based on contextual relationships among the objects is presented. Contextual rules used for classification and labeling allow a perfect understanding of the process and also an identification of the reasons why there are some errors in labeling. The time response of the algorithm is short and the accuracy attained is satisfactory. Furthermore, the computational requirements are not high. 2016 artículo científico 0121-750X https://www.redalyc.org/articulo.oa?id=498853954005 https://www.redalyc.org/journal/4988/498853954005/ https://www.redalyc.org/journal/4988/498853954005/html/ https://www.redalyc.org/journal/4988/498853954005/498853954005.epub https://www.redalyc.org/journal/4988/498853954005/movil en http://www.redalyc.org/revista.oa?id=4988 Ingeniería application/pdf Universidad Distrital Francisco José de Caldas Ingeniería (Colombia) Num.3 Vol.21 |
| format | Artículo científico |
| id | redalyc_498853954005 |
| language | en |
| publishDate | 2016 |
| publisher | Universidad Distrital Francisco José de Caldas |
| spellingShingle | 3D Semantic Modeling of Indoor Environments based on Point Clouds and Contextual Relationships Angie Quijano Ingeniería Kinect point cloud semantic modeling Indoor environment 3D Semantic Modeling of Indoor Environments based on Point Clouds and Contextual Relationships Angie Quijano Flavio Prieto Ingeniería Kinect point cloud semantic modeling Indoor environment Context: We propose a methodology to identify and label the components of a typical indoor environment in order to generate a semantic model of the scene. We are interested in identifying walls, ceilings, floors, doorways with open doors, doorways with closed doors that are recessed into walls, and partially occluded windows. Method: The elements to be identified should be flat in case of walls, floors, and ceilings and should have a rectangular shape in case of windows and doorways, which means that the indoor structure is Manhattan. The identification of these structures is determined through the analysis of the contextual relationships among them as parallelism, orthogonality, and position of the structure in the scene. Point clouds were acquired using a RGB-D device (Microsoft Kinect Sensor). Results: The obtained results show a precision of 99.03% and a recall of 95.68%, in a proprietary dataset. Conclusions: A method for 3D semantic labeling of indoor scenes based on contextual relationships among the objects is presented. Contextual rules used for classification and labeling allow a perfect understanding of the process and also an identification of the reasons why there are some errors in labeling. The time response of the algorithm is short and the accuracy attained is satisfactory. Furthermore, the computational requirements are not high. 2016 artículo científico 0121-750X https://www.redalyc.org/articulo.oa?id=498853954005 https://www.redalyc.org/journal/4988/498853954005/ https://www.redalyc.org/journal/4988/498853954005/html/ https://www.redalyc.org/journal/4988/498853954005/498853954005.epub https://www.redalyc.org/journal/4988/498853954005/movil en http://www.redalyc.org/revista.oa?id=4988 Ingeniería application/pdf Universidad Distrital Francisco José de Caldas Ingeniería (Colombia) Num.3 Vol.21 |
| title | 3D Semantic Modeling of Indoor Environments based on Point Clouds and Contextual Relationships |
| topic | Ingeniería Kinect point cloud semantic modeling Indoor environment |
| url | https://www.redalyc.org/articulo.oa?id=498853954005 https://www.redalyc.org/journal/4988/498853954005/ https://www.redalyc.org/journal/4988/498853954005/html/ https://www.redalyc.org/journal/4988/498853954005/498853954005.epub https://www.redalyc.org/journal/4988/498853954005/movil |