From Unstructured 3D Point Clouds to Structured Knowledge - A Semantics Approach

International audienc

Toward knowledge-based automatic 3D spatial topological modeling from LiDAR point clouds for urban areas

Le traitement d'un très grand nombre de données LiDAR demeure très coûteux et nécessite des approches de modélisation 3D automatisée. De plus, les nuages de points incomplets causés par l'occlusion et la densité ainsi que les incertitudes liées au traitement des données LiDAR compliquent la création automatique de modèles 3D enrichis sémantiquement. Ce travail de recherche vise à développer de nouvelles solutions pour la création automatique de modèles géométriques 3D complets avec des étiquettes sémantiques à partir de nuages de points incomplets. Un cadre intégrant la connaissance des objets à la modélisation 3D est proposé pour améliorer la complétude des modèles géométriques 3D en utilisant un raisonnement qualitatif basé sur les informations sémantiques des objets et de leurs composants, leurs relations géométriques et spatiales. De plus, nous visons à tirer parti de la connaissance qualitative des objets en reconnaissance automatique des objets et à la création de modèles géométriques 3D complets à partir de nuages de points incomplets. Pour atteindre cet objectif, plusieurs solutions sont proposées pour la segmentation automatique, l'identification des relations topologiques entre les composants de l'objet, la reconnaissance des caractéristiques et la création de modèles géométriques 3D complets. (1) Des solutions d'apprentissage automatique ont été proposées pour la segmentation sémantique automatique et la segmentation de type CAO afin de segmenter des objets aux structures complexes. (2) Nous avons proposé un algorithme pour identifier efficacement les relations topologiques entre les composants d'objet extraits des nuages de points afin d'assembler un modèle de Représentation Frontière. (3) L'intégration des connaissances sur les objets et la reconnaissance des caractéristiques a été développée pour inférer automatiquement les étiquettes sémantiques des objets et de leurs composants. Afin de traiter les informations incertitudes, une solution de raisonnement automatique incertain, basée sur des règles représentant la connaissance, a été développée pour reconnaître les composants du bâtiment à partir d'informations incertaines extraites des nuages de points. (4) Une méthode heuristique pour la création de modèles géométriques 3D complets a été conçue en utilisant les connaissances relatives aux bâtiments, les informations géométriques et topologiques des composants du bâtiment et les informations sémantiques obtenues à partir de la reconnaissance des caractéristiques. Enfin, le cadre proposé pour améliorer la modélisation 3D automatique à partir de nuages de points de zones urbaines a été validé par une étude de cas visant à créer un modèle de bâtiment 3D complet. L'expérimentation démontre que l'intégration des connaissances dans les étapes de la modélisation 3D est efficace pour créer un modèle de construction complet à partir de nuages de points incomplets.The processing of a very large set of LiDAR data is very costly and necessitates automatic 3D modeling approaches. In addition, incomplete point clouds caused by occlusion and uneven density and the uncertainties in the processing of LiDAR data make it difficult to automatic creation of semantically enriched 3D models. This research work aims at developing new solutions for the automatic creation of complete 3D geometric models with semantic labels from incomplete point clouds. A framework integrating knowledge about objects in urban scenes into 3D modeling is proposed for improving the completeness of 3D geometric models using qualitative reasoning based on semantic information of objects and their components, their geometric and spatial relations. Moreover, we aim at taking advantage of the qualitative knowledge of objects in automatic feature recognition and further in the creation of complete 3D geometric models from incomplete point clouds. To achieve this goal, several algorithms are proposed for automatic segmentation, the identification of the topological relations between object components, feature recognition and the creation of complete 3D geometric models. (1) Machine learning solutions have been proposed for automatic semantic segmentation and CAD-like segmentation to segment objects with complex structures. (2) We proposed an algorithm to efficiently identify topological relationships between object components extracted from point clouds to assemble a Boundary Representation model. (3) The integration of object knowledge and feature recognition has been developed to automatically obtain semantic labels of objects and their components. In order to deal with uncertain information, a rule-based automatic uncertain reasoning solution was developed to recognize building components from uncertain information extracted from point clouds. (4) A heuristic method for creating complete 3D geometric models was designed using building knowledge, geometric and topological relations of building components, and semantic information obtained from feature recognition. Finally, the proposed framework for improving automatic 3D modeling from point clouds of urban areas has been validated by a case study aimed at creating a complete 3D building model. Experiments demonstrate that the integration of knowledge into the steps of 3D modeling is effective in creating a complete building model from incomplete point clouds

Points clouds generation using TLS and dense-matching techniques. A test on approachable accuracies of different tools

3D detailed models derived from digital survey techniques has increasingly developed and focused in many field of application, ranging from the land and urban areas survey, using remote sensed data, to landscape assets and finally to Cultural Heritage items. The high detailed content and accuracy of such models makes them so attractive and usable for large sets of purposes. The present paper is focused on a test aimed to point clouds generation fulfilled by archaeological data; active and passive sensors techniques and related image matching systems have been used in order to evaluate and compare the accuracy of results, achievable using proper TLS and low cost image-matching software and techniques. After a short review of approachable methods some attained results will be discussed; the test area consists of a set of mosaic floorings in a late roman domus located in Aquileia (UD-Italy) requesting a very high level of details and high scale and precision. The experimental section provides the descriptions of the applied tests in order to compare the different software and the employed method

Developing an interoperable cloud-based visualization workflow for 3D archaeological heritage data. The Palenque 3D Archaeological Atlas

In archaeology, 3D data has become ubiquitous, as researchers routinely capture high resolution photogrammetry and LiDAR models and engage in laborious 3D analysis and reconstruction projects at every scale: artifacts, buildings, and entire sites. The raw data and processed 3D models are rarely shared as their computational dependencies leave them unusable by other scholars. In this paper we outline a novel approach for cloud-based collaboration, visualization, analysis, contextualization, and archiving of multi-modal giga-resolution archaeological heritage 3D data. The Palenque 3D Archaeological Atlas builds on an open source WebGL systems that efficiently interlink, merge, present, and contextualize the Big Data collected at the ancient Maya city of Palenque, Mexico, allowing researchers and stakeholders to visualize, access, share, measure, compare, annotate, and repurpose massive complex archaeological datasets from their web-browsers

GEOMATIC CONTRIBUTION FOR THE RESTORATION PROJECT OF THE VALENTINO CASTLE GREEN ROOM. FROM DATA ACQUISITION TO INTEGRATED DOCUMENTATION

In the framework of restoration operations, valuable assistance can be supplied from innovative techniques and methods developed in the field of Geomatics. Over the years, this continuous collaboration has produced synergistic and interdisciplinary results that have been successfully contributing to heritage conservation and valorisation. In the case of the current research, thorough multisensory investigations have been performed in order to provide a deeper knowledge of the Green Room of the Valentino Castle in Turin and to support the planning of the future restoration works that will involve this valuable asset. In the framework of this experience, four LiDAR systems have been employed in order to evaluate the different results obtainable from the sensors. Additionally, a complete photogrammetric close-range survey has been carried out, and some tests were completed using a hyperspectral camera. The workflow followed during the current research is described in this paper, and a comparison between the obtained outputs is proposed, focusing on the characteristics of these metric products, useful and sometimes necessary in the framework of the restoration project. Besides, some considerations on the advantages and the issues connected with the use of these reality-based data as a starting point for HBIM (Heritage Building Information Modeling) model generation are proposed, along with some observations about the potentialities of a photogrammetric co-registration approach using spectrum technologies for deterioration/decay detection and monitoring of heritage

Geomatics tools to record 3D shapes for intervention planning

The paper offers a state of art of Geomatics tools that it is possible to use after a natural and/or human disaster on urban centers or natural landscapes to record the 3D shape. This knowledge is important both for first aid initiatives devoted to safeguard human lives and for support decision on first technical interventions. The same data, if correctly recorded, are the basic step to plan recovering actions and reconstruction strategies. The high automation level of the metric survey techniques open unsolved questions about the correct use of automatic tools both to acquire primary data and the appropriate management of them to give affordable and accurate metric information to the specialists. Image based technologies (e.g. 3D photogrammetry, SFM) and range based instruments (e.g. terrestrial and aerial laser scanning systems) are analyzed in terms of best rules to acquire the necessary primary data by highlighting the most common mistakes that automation approach could generate; the same analysis is developed for the software used to manage those primary data where automation processing are in many cases not well understood. A more skilled use of primary data acquisition instruments and management software will allow a better quality of the resulting 3D models also considering the real needs in the different phases of the emergency after disasters

BIM Application for the Basilica of San Marco in Venice: Procedures and Methodologies for the Study of Complex Architectures

The BIM (Building Information Model) of the Basilica of San Marco contains the solutions to the many problems encountered during its acquisition and modelling stage. The complexity of the church and the variety of its materials (golden mosaics, capitals of different styles and origins, statues and decorations in many different marble types), the large and continuous stream of visitors, and the request for high-resolution models and orthophotos forced us to devise a strategy for the digitization process: a multiscale photogrammetric approach allowed us to acquire all materials and decorations of the basilica and, according to the use of a reference topographic network, we could split the whole work into smaller parts. Later, in the modelling stage, the decision to use a non-commercial BIM software allowed us to use NURBS (non-uniform rational B-spline) for a more accurate restitution of architectural elements and decorations and to integrate high-resolution orthophotos for the description of all surfaces (both marbles and golden mosaics). The established workflow started with the initial acquisition of images and resulted in both final models and high-quality orthophotos, so we were able to obtain different outcomes to answer the specific needs of the church, its managers, and its users

Suitability of Automatic Photogrammetric Reconstruction Configurations for Small Archaeological Remains

19 p.Three-dimensional (3D) reconstruction is a useful technique for the documentation, characterization, and evaluation of small archeological objects. In this research, a comparison among different photogrammetric setups that use different lenses (macro and standard zoom) and dense point cloud generation calibration processes for real specific objects of archaeological interest with different textures, geometries, and materials is raised using an automated data collection. The data acquisition protocol is carried out from a platform with control points referenced with a metrology absolute arm to accurately define a common spatial reference system. The photogrammetric reconstruction is performed considering a camera pre-calibration as well as a self-calibration. The latter is common for most data acquisition situations in archaeology. The results for the different lenses and calibration processes are compared based on a robust statistical analysis, which entails the estimation of both standard Gaussian and non-parametric estimators, to assess the accuracy potential of different configurations. As a result, 95% of the reconstructed points show geometric discrepancies lower than 0.85 mm for the most unfavorable case and less than 0.35 mm for the other casesS

Using 3D laser scanner data for 3D modelling: “el Born” in Barcelona

Peer Reviewe

A semantic-based platform for the digital analysis of architectural heritage

This essay focuses on the fields of architectural documentation and digital representation. We present a research paper concerning the development of an information system at the scale of architecture, taking into account the relationships that can be established between the representation of buildings (shape, dimension, state of conservation, hypothetical restitution) and heterogeneous information about various fields (such as the technical, the documentary or still the historical one). The proposed approach aims to organize multiple representations (and associated information) around a semantic description model with the goal of defining a system for the multi-field analysis of buildings