Modelado Semántico 3D de Ambientes Interiores basado en Nubes de Puntos y Relaciones Contextuales
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Universidad Distrital Francisco José de Caldas
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Contexto: Se propone una metodología para identificar y etiquetar los componentes de la estructura de un ambiente interior típico y así generar un modelo semántico de la escena. Nos interesamos en la identificación de: paredes, techos, suelos, puertas abiertas, puertas cerradas que forman un pequeño hueco con la pared y ventanas parcialmente ocultas.Método: Los elementos a ser identificados deben ser planos en el caso de paredes, pisos y techos y deben tener una forma rectangular en el caso de puertas y ventanas, lo que significa que la estructura del ambiente interior es Manhattan. La identificación de estas estructuras se determina mediante el análisis de las relaciones contextuales entre ellos, paralelismo, ortogonalidad y posición de la estructura en la escena. Las nubes de puntos de las escenas fueron adquiridas con un dispositivo RGB-D (Sensor Kinect de Microsoft).Resultados: Los resultados obtenidos muestran una precisión de 99.03% y una sensibilidad de 95.68%, usando una base de datos propia.Conclusiones: Se presenta un método para el etiquetado semántico 3D de escenas en interiores basado en relaciones contextuales entre los objetos. Las reglas contextuales usadas para clasificación y etiquetado permiten un buen entendimiento del proceso y, también, una identificación de las razones por las que se presentan algunos errores en el etiquetado. El tiempo de respuesta del algoritmo es corto y la exactitud alcanzada es satisfactoria. Además, los requerimientos computacionales no son altos.
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.
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.
Palabras clave
Indoor environment, Kinect, point cloud, semantic modeling, Ambientes interiores, Kinect, modelado semántico, nube de puntos.