Thereafter points are segmented into planar patches based on the Random Sample Consensus (RANSAC) technique, as most facades are dominated by planar faces. An initial building point cluster detection and roughness based point separation within the cluster itself are the preliminary stages of this process. After filtering of the point clouds, the building façade extraction takes place. In this paper, a new approach for automatic and fast processing of MLS data for the detection of building patches while restricting to segment other features is introduced. Point cloud segmentation and recognition are the most important steps in this context. Automatic processing of MLS point clouds for feature extraction on building facades is a demanding work. Por lo tanto, el propósito de este trabajo es proveer una revisión en el estado del conocimiento acerca de las técnicas desarrolladas en las diferentes fases que se llevan a cabo con el tratamiento de estas nubes de puntos, como el registro y georreferenciación, la segmentación, clasificación y modelado tridimensional.Ĭurrently, data captured by Mobile Laser Scanners (MLS) is becoming a leading source for the modelling of building façade geometry. Esta información recolectada se representa en forma de nubes de puntos que se utilizan en una amplia variedad de aplicaciones como la planeación del crecimiento urbano, el análisis estructural de construcciones, el modelado de fenómenos de erosión y deforestación, la documentación de patrimonio histórico y la navegación virtual a través de los sitios turísticos mediante modelos tridimensionales de edificaciones. Este instrumento tiene la capacidad de capturar información topográfica y geométrica de cualquier estructura con precisiones hasta el orden de los milímetros, y en algunos casos también captura la información radiométrica de los objetos escaneados. The results show successful detection rates of 78% and 94% using 3D and 2D approaches respectively.Įn este artículo se presenta una revisión del estado del arte de las diferentes metodologías que se han desarrollado para el tratamiento digital de nubes de puntos tridimensionales, recolectadas mediante un escáner láser terrestre. A 2D image processing scheme is also presented to find the curbs as edges in a generated 2D height image. The curb can be isolated from the rest of the ground objects based on the previous parameters in addition to elevation gradient within the local neighborhood. These parameters can be used to extract the ground objects, such as curb, street floor and sidewalk. This is done by computing the surface normal direction and the normalized eigenvalues. Features such as the road curb can be extracted by analyzing the local neighborhood of every point. The proposed pipeline utilizes a covariance- based procedure to perform a 3D segmentation of point clouds. ![]() In this paper a pipeline for point cloud processing to detect the road curb from unorganized point clouds captured from a mobile terrestrial laser scanner is proposed. The detection of different road furniture such as curb, street floor and sidewalk from point clouds is important in many applications such as road maintenance and city planning. Experiments in this paper show that the algorithm achieves around 97% in both F1-score and model accuracy for ground point labelling. The method successfully labels ground and non-ground points in the presence of steep slopes and height discontinuity in the terrain. The performance of the new method has been demonstrated through two ALS datasets covering urban environments. The new algorithm does not require transforming data into regular 3D voxel grids or any rasterization. This method is free from the limitations of point clouds’ irregular data structure and varying data density, which is the biggest challenge for using the elegant convolutional neural network. It studies feature relevance, and investigates three models that are different combinations of features. This paper introduces a novel local feature-based non end-to-end DL algorithm that generates a binary classifier for ground point filtering. The main advantage of using a supervised non end-to-end approach is that it requires less training data. One of the main challenges of using supervised DL approaches is getting a sufficient amount of training data. DL methods used for classification can be categorized into end-to-end and non end-to-end approaches. Recently, Deep Learning (DL) has become the most dominant technique for 3D point cloud classification. ![]() Many methods have been developed over the last three decades. ![]() Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management.
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