Multi-scale conditional random fields for over-segmented irregular 3D point clouds classification

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holderIn this paper, we propose using multi-scale Conditional Random Fields to classes 3D outdoor terrestrial laser scanned data. We improved Lim and Suterpsilas methods by introducing regional edge potentials in addition to the local edge and node potentials in the multi-scale Conditional Random Fields, and only a relatively small amount of increment in the computation time is required to achieve the improved recognition rate. In the model, the raw data points are over-segmented into an improved mid-level representation, ldquosuper-voxelsrdquo. Local and regional features are then extracted from the super-voxel and parameters learnt by the multi-scale Conditional Random Fields. The classification accuracy is improved by 5% to 10% with our proposed model compared to labeling with Conditional Random Fields in (Lim and Suter, 2007). The overall computation time by labeling the super-voxels instead of individual points is lower than the previous 3D data labeling approaches.Ee Hui Lim, David Sute

Integration of range and image data for building reconstruction

The extraction of information from image and range data is one of the main research topics. In literature, several papers dealing with this topic has been already presented. In particular, several authors have suggested an integrated use of both range and image information in order to increase the reliability and the completeness of the results exploiting their complementary nature. In this paper, an integration between range and image data for the geometric reconstruction of man-made object is presented. The focus is on the edge extraction procedure performed in an integrated way exploiting both the from range and image data. Both terrestrial and aerial applications have been analysed for the faade extraction in terrestrial acquisitions and the roof outline extraction from aerial data. The algorithm and the achieved results will be described and discussed in detail

Fusion of 3D models derived from TLS and image-based techniques for CH enhanced documentation

Recognizing the various advantages offered by 3D new metric survey technologies in the Cultural Heritage documentation phase, this paper presents some tests of 3D model generation, using different methods, and their possible fusion. With the aim to define potentialities and problems deriving from integration or fusion of metric data acquired with different survey techniques, the elected test case is an outstanding Cultural Heritage item, presenting both widespread and specific complexities connected to the conservation of historical buildings. The site is the Staffarda Abbey, the most relevant evidence of medieval architecture in Piedmont. This application faced one of the most topical architectural issues consisting in the opportunity to study and analyze an object as a whole, from twice location of acquisition sensors, both the terrestrial and the aerial one. In particular, the work consists in the evaluation of chances deriving from a simple union or from the fusion of different 3D cloudmodels of the abbey, achieved by multi-sensor techniques. The aerial survey is based on a photogrammetric RPAS (Remotely piloted aircraft system) flight while the terrestrial acquisition have been fulfilled by laser scanning survey. Both techniques allowed to extract and process different point clouds and to generate consequent 3D continuous models which are characterized by different scale, that is to say different resolutions and diverse contents of details and precisions. Starting from these models, the proposed process, applied to a sample area of the building, aimed to test the generation of a unique 3Dmodel thorough a fusion of different sensor point clouds. Surely, the describing potential and the metric and thematic gains feasible by the final model exceeded those offered by the two detached models

Proposição metodológica de extração da altimetria em edificações utilizando dados LIDAR com vista a estudos volumétricos de coeficientes de aproveitamento

O aperfeiçoamento de instrumentos para o planejamento urbano torna-se cada vez mais importante no contexto das cidades. O Estatuto da Cidade, Lei 10.257 de 2001, configura-se hoje como importante recurso que apresenta novos instrumentos para favorecer a gestão urbana, com vistas a garantir que a propriedade cumpra a sua função social e que as cidades possam se adequar às necessidades ambientais e humanas. Entre esses instrumentos à disposição do gestor urbano, estão a Outorga Onerosa do Direito de Construir (OODC) e a Transferência do Direito de Construir (TDC), que concedem o direito ao proprietário de edificar em patamares maiores que o estabelecido pelo Coeficiente de Aproveitamento (CA). Contudo, a definição das áreas adequadas para receberem essas volumetrias adicionais deve ser muito bem estudada, daí o interesse na aplicação de metodologias de geoprocessamento que favoreçam a caracterização e análise da paisagem volumétrica da cidade. O CA é a relação entre a área edificável total (somadas as áreas de todos os andares) e área do terreno, que define o volume final possível ou o envelope máximo para as edificações..

Geometric refinement of laser-derived building roof contours using photogrammetric data

In this paper, a methodology is proposed for the geometric refinement of laser scanning building roof contours using high-resolution aerial images and Markov Random Field (MRF) models. The proposed methodology takes for granted that the 3D description of each building roof reconstructed from the laser scanning data (i.e., a polyhedron) is topologically correct and that it is only necessary to improve its accuracy. Since roof ridges are accurately extracted from laser scanning data, our main objective is to use high-resolution aerial images to improve the accuracy of roof outlines. In order to meet this goal, the available roof contours are first projected onto the image-space. After that,  the projected polygons and the straight lines extracted from the image are used to establish an MRF description, which is based on relations (relative length, proximity, and orientation) between the two sets of straight lines. The energy function associated with the MRF is minimized by using a modified version of the brute force algorithm, resulting in the grouping of straight lines for each roof object. Finally, each grouping of straight lines is topologically reconstructed based on the topology of the corresponding laser scanning polygon projected onto the image-space. The preliminary results showed that the proposed methodology is promising, since most sides of the refined polygons are geometrically better than corresponding projected laser scanning straight lines.Neste artigo uma metodologia é proposta para o refinamento geométrico de contornos de telhados extraídos de dados de varredura a laser, usando imagens aéreas de alta resolução e modelos de campo aleatório de Markov (MRF - Markov Random Field). A metodologia proposta assume que a descrição 3D (isto é, um poliedro) de cada telhado de edifício reconstruído de dados de varredura a laser está topologicamente correta e que é necessário apenas melhorar sua acurácia. Visto que as cumeeiras de telhado são acuradamente extraídas de dados de varredura a laser, o objetivo básico é usar imagens aéreas de  alta resolução para melhorar somente a qualidade geométrica dos contornos de telhado. A fim de atingir esta meta, os contornos 3D representando contornos de telhados são primeiramente transformados para o espaço imagem. Na seqüência, as retas extraídas da imagem e as retas resultantes dos polígonos projetados são utilizadas para estabelecer uma descrição MRF com base em relações (de comprimento, proximidade e orientação) entre ambos os conjuntos de retas. A função de energia associada com a descrição MRF é minimizada através de uma versão modificada do algoritmo de força bruta, resultando num agrupamento de retas para  cada contorno de telhado. Finalmente, cada agrupamento de retas é topologicamente reconstruído baseando-se na topologia do correspondente polígono projetado no espaço imagem. Os resultados obtidos mostraram que a metodologia proposta é promissora, visto que geralmente os polígonos refinados são geometricamente melhores que os correspondentes polígonos resultantes da projeção dos contornos 3D de telhados

EXTRAÇÃO AUTOMÁTICA DE CONTORNOS DE TELHADOS USANDO DADOS DE VARREDURA A LASER E CAMPOS RANDÔMICOS DE MARKOV

This paper proposes a methodology for automatic extraction of building roofcontours from a Digital Elevation Model (DEM), which is generated through theregularization of an available laser point cloud. The methodology is based on twosteps. First, in order to detect high objects (buildings, trees etc.), the DEM issegmented through a recursive splitting technique and a Bayesian mergingtechnique. The recursive splitting technique uses the quadtree structure forsubdividing the DEM into homogeneous regions. In order to minimize thefragmentation, which is commonly observed in the results of the recursive splittingsegmentation, a region merging technique based on the Bayesian framework isapplied to the previously segmented data. The high object polygons are extracted byusing vectorization and polygonization techniques. Second, the building roofcontours are identified among all high objects extracted previously. Taking intoaccount some roof properties and some feature measurements (e. g., area,rectangularity, and angles between principal axes of the roofs), an energy functionwas developed based on the Markov Random Field (MRF) model. The solution ofthis function is a polygon set corresponding to building roof contours and is foundby using a minimization technique, like the Simulated Annealing (SA) algorithm. Experiments carried out with laser scanning DEM´s showed that the methodologyworks properly, as it delivered roof contours with approximately 90% shapeaccuracy and no false positive was verified.Este artigo propõe uma metodologia para a extração automática de contornos detelhados de edifícios a partir de um MDE (Modelo Digital de Elevação), gerado apartir da regularização de uma malha irregular de dados laser preexistentes. Ametodologia baseia-se em duas etapas. Primeiramente, a fim de detectar objetosaltos (edifícios altos, árvores etc.), o MDE é segmentado através de uma técnica dedivisão recursiva e de uma técnica de fusão bayesiana. A técnica de divisãorecursiva usa a estrutura quadtree para subdividir o MDE em regiões homogêneas.A fim de minimizar a fragmentação que freqüentemente é observada nos resultadosda segmentação por divisão recursiva, uma técnica de fusão baseada em InferênciaBayesiana é aplicada aos dados previamente segmentados. Os contornos dos objetos altos são obtidos através de técnicas de vetorização e poligonização. Na segundaetapa, os contornos de telhados de edifícios são identificados entre todos os objetosaltos extraídos previamente. Levando em conta algumas propriedades de telhado ealguns atributos (por exemplo, área, retangularidade e ângulos entre os eixosprincipais dos telhados), uma função de energia foi desenvolvida com base nomodelo Markov Random Field (MRF). A solução desta função é um conjunto depolígonos representando contornos de telhados de edifícios e é encontrada atravésde técnicas de minimização, como o algoritmo Simulated Annealing (SA). Váriosexperimentos foram realizados com base em DEM´s obtidos a partir de dados devarredura a laser, os quais demonstraram que a metodologia proposta funcionaadequadamente, visto que foram extraídos contornos de telhados comaproximadamente 90% de completeza de área e nenhum falso positivo foiverificado

Automated 3D scene reconstruction from open geospatial data sources: airborne laser scanning and a 2D topographic database

Open geospatial data sources provide opportunities for low cost 3D scene reconstruction. In this study, based on a sparse airborne laser scanning (ALS) point cloud (0.8 points/m2) obtained from open source databases, a building reconstruction pipeline for CAD building models was developed. The pipeline includes voxel-based roof patch segmentation, extraction of the key-points representing the roof patch outline, step edge identification and adjustment, and CAD building model generation. The advantages of our method lie in generating CAD building models without the step of enforcing the edges to be parallel or building regularization. Furthermore, although it has been challenging to use sparse datasets for 3D building reconstruction, our result demonstrates the great potential in such applications. In this paper, we also investigated the applicability of open geospatial datasets for 3D road detection and reconstruction. Road central lines were acquired from an open source 2D topographic database. ALS data were utilized to obtain the height and width of the road. A constrained search method (CSM) was developed for road width detection. The CSM method was conducted by splitting a given road into patches according to height and direction criteria. The road edges were detected patch by patch. The road width was determined by the average distance from the edge points to the central line. As a result, 3D roads were reconstructed from ALS and a topographic database

Implementación del Laboratorio Urbano de Brno (República Checa) en el entorno BRAIN. Fase I.b.

Con el paso del tiempo las edificaciones se encuentran expuestas a agentes externos que repercuten en su envejecimiento, los diversos procesos físicos, químicos, efectos medioambientales, biológicos, entre otros muchos, representan los principales factores influyentes en el deterioro progresivo de las mismas, reduciendo su vida útil. Las fachadas constituyen la parte de la edificación que se encuentra más expuesta a estos efectos degradantes, por lo que el interés de esta investigación y de la metodología que sigue, se enfoca en el análisis de las lesiones en el frente urbano conformado por las fachadas expuestas a vial. A medida que los elementos que componen las fachadas de la edificación se van degradando, se empiezan a reflejar síntomas que manifiestan a nivel macro el deterioro, con el subsiguiente problema de gestión de la intervención sobre el parque edificado