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Measuring the Short-Term degradation of Stone in Historical Buildings

INTERNATIONAL JOURNAL OF ARCHITECTURAL HERITAGE 2024年第10期18卷 1571-1595页

作者： Quintana, Blanca Adan, Antonio Ramon, Amanda UNED Dept Ingn Electr Elect Control Telemat & Quim Apli ETSI Ind Madrid Spain Univ Castilla La Mancha 3D Visual Comp & Robot Lab Ciudad Real Spain UNED Dept Ingn Electr Elect Control Telematica&Quim Apl ETSI Ind CJuan Rosal 12 Madrid 28040 Spain

This paper proposes a methodology with which to measure the slight erosion undergone by valuable heritage pieces in a short period of time. The problems dealt with by our method concern the irregular (free-shape) geometry of the piece to be measured, the difficulty involved in achieving the fine registration required among different samples of the piece, the slight loss of material in a short period of time and the suitable definition of descriptors that will help to carry out a reliable qualitative and quantitative analysis. This document proposes a basic methodology that is able to tackle each of the aforementioned problems. An in-depth explanation of the degradation analysis has been carried out on a representative case study, including the qualitative and quantitative procedures employed. The results obtained lead us to believe that this methodology could be reproduced and applied to similar degradation problems related to old buildings.

关键词： 3d data processing deformation measurement heritage preservation laser scanning point clouds stone degradation assessment

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deep Neural Networks for Geometric Shape deformation 1

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45th German Conference on Artificial Intelligence (KI)

作者： Farahani, Aida Vitay, Julien Hamker, Fred H. Tech Univ Chemnitz Str Nationen 62 D-09111 Chemnitz Germany

ISBN: (数字)9783031157912

ISBN: (纸本)9783031157912;9783031157905

Geometric deep learning is a promising approach to bring the representational power of deep neural networks to 3d data. Explicit 3d representations such as point clouds or meshes can have varying and often a huge number of dimensions, what limits their use as an input to a neural network. Implicit representations such as signed distance functions (SdF) are on the contrary low-dimensional and fixed representations of the structure of a 3d shape that can be easily fed into a neural network. In this paper, we demonstrate how deep SdF neural networks can be used to precisely predict the deformation of a material after the application of a specific force. The model is trained using a set of custom finite element simulations in order to generalize to unseen forces.

关键词： Geometric deep learning Implicit neural representation Geometric deformation modeling FEM simulations 3d data processing Signed distance Functions

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Face Mask Identification Using Spatial and Frequency Features in depth Image from Time-of-Flight Camera

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SENSORS 2023年第3期23卷 1596-1596页

作者： Wang, Xiaoyan Xu, Tianxu An, dong Sun, Lei Wang, Qiang Pan, Zhongqi Yue, Yang Nankai Univ Inst Modern Opt Tianjin 300350 Peoples R China Zhengzhou Univ Natl Ctr Int Joint Res Elect Mat & Syst Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Shphotonics LLC Tianjin 300450 Peoples R China Angle AI Tianjin Technol Co Ltd Tianjin 300450 Peoples R China Univ Louisiana Lafayette Dept Elect & Comp Engn Lafayette LA 70504 USA Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China

Face masks can effectively prevent the spread of viruses. It is necessary to determine the wearing condition of masks in various locations, such as traffic stations, hospitals, and other places with a risk of infection. Therefore, achieving fast and accurate identification in different application scenarios is an urgent problem to be solved. Contactless mask recognition can avoid the waste of human resources and the risk of exposure. We propose a novel method for face mask recognition, which is demonstrated using the spatial and frequency features from the 3d information. A ToF camera with a simple system and robust data are used to capture the depth images. The facial contour of the depth image is extracted accurately by the designed method, which can reduce the dimension of the depth data to improve the recognition speed. Additionally, the classification process is further divided into two parts. The wearing condition of the mask is first identified by features extracted from the facial contour. The types of masks are then classified by new features extracted from the spatial and frequency curves. With appropriate thresholds and a voting method, the total recall accuracy of the proposed algorithm can achieve 96.21%. Especially, the recall accuracy for images without mask can reach 99.21%.

关键词： 3d data processing depth camera face mask identification

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Automatic generation of as-is BEM models of buildings

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JOURNAL OF BUILdING ENGINEERING 2023年 73卷

作者： Adan, A. Ramon, A. Vivancos, J. L. Vilar, A. Aparicio-Fernandez, A. Univ Castilla La Mancha Visual Comp & Robot Lab 3D Ciudad Real Spain Univ Politecn Valencia Project Management Innovat & Sustainabil Res Ctr P Valencia 46022 Spain Univ Politecn Valencia Ctr Invest Tecnol Edificac CITE Valencia 46022 Spain

This paper shows a method with which to create a proprietary as-is 3d semantic thermal model of a building and transform it into standard BEM formats. Geometric and relational information regarding the essential structural elements of the building (i.e., zone, ceiling, floor, wall, door, etc.) is, together with their corresponding as-is temperatures, first extracted from thermal points clouds of the scene and then adapted to the standard geometric BEM requirements. Nongeometric properties concerning definitions and properties of envelopes, windows and doors are also defined as essential information. After all this information has been collected, a BEM file is eventually generated in order to be used in energy simulation software. One of the specific features of this work is the generation of as-is BIM formats of the interior of a building that are later adapted for energy simulation applications in EnergyPlus and TRNSYS software. An ASCII file containing the temperatures of the main structural elements of the building is also created. The method has been tested on real thermal point clouds of the ground floor of a building, yielding promising results.

关键词： 3d data processing Thermal point clouds 3d semantic thermal model Scan-to-BEM

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Robot for thermal monitoring of buildings

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AUTOMATION IN CONSTRUCTION 2023年第1期154卷

作者： Adan, Antonio Lopez-Rey, Alejandro Ramon, Amanda Univ Castilla La Mancha 3D Visual Comp & Robot Lab Ciudad Real Spain

Until the last decade, robots for thermal monitoring of buildings were limited to measuring ambient temperature. Recent research has focused on obtaining detailed thermal information about the structural components of buildings since the mid-2010s. This paper presents the methodology and results of the MoPAd2 (Mobile Platform for Autonomous digitization) robot, an autonomous, low-cost, small-sized mobile robot equipped with a thermal scanning platform. The platform generates 3d thermal models of building interiors over time and conducts repeated data acquisition sessions, saving energy during waiting intervals. The paper explains how a dense thermal point cloud is obtained from different locations in the scene and processed to create a single 3d thermal model, and two case studies are presented. This robotics system has the potential to revolutionize thermal monitoring of buildings and enable energy characterization through thermal models.

关键词： Robotics digital twins Building digitisation Thermal point clouds 3d data processing

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Thermal point clouds of buildings: A review

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ENERGY ANd BUILdINGS 2022年 274卷

作者： Ramon, Amanda Adan, Antonio Javier Castilla, Francisco Univ Castilla La Mancha 3D Visual Comp & Robot Lab E-13071 Ciudad Real Spain Univ Castilla La Mancha Civil Engn & Construct Dept E-13071 Ciudad Real Spain

New methodologies that explore and identify energy problems of buildings can be defined by using three-dimensional thermal models over time. The acquisition of thermal point clouds is the primary link in a chain that ends with a semantic Building Information Model (BIM) that contains energetic parame-ters. This literature review attempts to outline the current state of the art as regards the most important stages in this path by discussing the topics of thermal point cloud acquisition systems, 3d thermal semantic models, exportation to thermal models and the current applications of thermal point clouds in Architecture, Engineering and Construction. Special mention is made of the limitations and gaps in each stage. As a summary, it can be stated that only simple thermal BIM models of single constructive elements have been defined to date, and that a long path must be followed in order to automatically obtain a thermal BIM model of a multi-story building. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Thermal point clouds Building energy model Infrared thermography 3d data processing Building information modelling

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3d point cloud density-based segmentation for vine rows detection and localisation

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COMPUTERS ANd ELECTRONICS IN AGRICULTURE 2022年 199卷

作者： Biglia, A. Zaman, S. . Gay, P. Aimonino, d. Ricauda Comba, L. Univ Torino Dept Agr Forest & Food Sci DiSAFA Largo Paolo Braccini 2 I-10095 Grugliasco TO Italy Dimens Agri Technol AS Berghagan 3 N-1405 Langhus Norway CNR IEIIT Politecn Torino Corso Duca degli Abruzzi 24 I-10129 Turin Italy

The adoption of new sensors for crop monitoring is leading to the acquisition of large amounts of data, which usually are not directly usable for agricultural applications. The 3d point cloud maps of fields and parcels, generated from remotely sensed data, are examples of such big data, which require the development of specific algorithms for their processing and interpretation, with the final aim to obtain valuable information about crop status. This manuscript presents an innovative 3d point cloud processing algorithm for vine row detection and localisation within vineyard maps, based on the detection of key points and a density-based clustering approach. Vine row localisation is a crucial phase in the interpretation of the complex and huge 3d point clouds of agricultural environments, which makes it possible to move the focus from a macro level (parcel and plot scale) to a micro level (plants, fruits and branches). The algorithm outputs fully describe the spatial location of each vine row within the whole 3d model of the agricultural environment by a set of key points and an interpolating curve. The algorithm is specifically conceived to be robust and: (i) independent of the adopted airborne sensor used to acquire the in-field data (not requiring a model with colour or spectral information);(ii) able to manage vine-yards with any vine row layout or orientation (such as curvilinear) and (iii) not hindered by the occurrence of missing plants. The experimental results, obtained by processing the models of seven case study parcels, proved the algorithm's reliability and accuracy: the automatic vine row detection was found to be 100% in accordance with the manual one;and the obtained localisation indices showed an average error of 12 cm and standard deviation of 10 cm, which is fully compatible with the considered agricultural applications. In addition, the algorithm outputs can be profitably exploited for enhanced path planning of autonomous agricultural machines ado

关键词： Smart agriculture Precision viticulture Remote sensing from UAV Photogrammetry 3d data processing

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An autonomous robotic platform for automatic extraction of detailed semantic models of buildings

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AUTOMATION IN CONSTRUCTION 2020年 109卷 102963-000页

作者： Adan, A. Quintana, B. Prieto, S. A. Bosche, F. Univ Castilla La Mancha 3D Visual Comp & Robot Lab Ciudad Real Spain Univ Edinburgh Inst Infrastruct & Environm Edinburgh Midlothian Scotland

This paper presents an integrated system that automatically provides detailed as-is semantic 3d models of buildings. The system is able to explore and reconstruct large scenes at a high level of detail, passing through five semantic levels, finally generating a detailed semantic model of the building. Our autonomous scanning platform collects raw data regarding the scene. At the first level of modelling, our autonomous scanning platform collects data regarding the scene and generates a point cloud that is later structured in a semantic point cloud model containing indoor, clutter and outlier point clouds. The second and third levels of semantic models consist of a simple B-rep representation and a model of basic building components, which includes the walls, ceiling, floor and columns, as well as their topology. Openings are then added, thus yielding our fourth semantic model. Finally, small components in buildings, such as sockets, switches, lights and others are recognised, resulting in the fifth semantic model. This approach has been tested on real data of building floors using our Mobile Platform for Autonomous digitization (MoPAd). To the authors' knowledge this is the first work that, after obtaining 3d data with an autonomous mobile scanning platform, achieves such detailed modelling of building interiors. The performance of the method has been assessed quantitatively against ground truth on simulated and real environments. Two videos are available at the Supplementary material of this paper.

关键词： Robotic platform Mobile mapping 3d data processing Semantic modelling Building information model Scan-to-BIM

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Transfer Learning in urban object classification: Online images to recognize point clouds

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AUTOMATION IN CONSTRUCTION 2020年第0期111卷 103058-000页

作者： Balado, Jesus Sousa, Ricardo diaz-Vilarino, Lucia Arias, Pedro Univ Vigo Appl Geotechnol Res Grp Dept Nat Resources & Environm Engn Sch Min & Energy Engn Campus Lagoas Marcosende Vigo 36310 Spain Univ Porto LIAAD INESC TEC Campus Fac Engn P-4200465 Porto Portugal Univ Vigo Sch Ind Engn Dept Design Engn Appl Geotechnol Res Grp Campus Lagoas Marcosende Vigo 36310 Spain

The application of deep Learning techniques to point clouds for urban object classification is limited by the large number of samples needed. Acquiring and tagging point clouds is more expensive and tedious labour than its image equivalent process. Point cloud online datasets contain few samples for deep Learning or not always the desired classes This work focuses on minimizing the use of point cloud samples for neural network training in urban object classification. The method proposed is based on the conversion of point clouds to images (pc-images) because it enables: the use of Convolutional Neural Networks, the generation of several samples (images) per object (point clouds) by means of multi-view, and the combination of pc-images with images from online datasets (ImageNet and Google Images). The study is conducted with ten classes of objects extracted from two street point clouds from two different cities. The network selected for the job is the InceptionV3. The training set consists of 5000 online images with a variable percentage (0% to 10%) of pc-images. The validation and testing sets are composed exclusively of pc-images. Although the network trained only with online images reached 47% accuracy, the inclusion of a small percentage of pc-images in the training set improves the classification to 99.5% accuracy with 6% pc-images. The network is also applied at IQmulus & TerraMobilita Contest dataset and it allows the correct classification of elements with few samples.

关键词： LidAR CNN Inception 3d data processing Mobile laser scanning data fusion

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Obtaining and Monitoring Warehouse 3d Models with Laser Scanner data

Obtaining and Monitoring Warehouse 3D Models with Laser Scan...

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3rd Iberian Robotics Conference (Robot)

作者： Adan, Antonio de La Rubia, david Vazquez, Andres S. Univ Castilla La Mancha Visual Comp & Robot Lab 3D E-13071 Ciudad Real Spain

ISBN: (纸本)9783319708362;9783319708355

This paper is focused on creating semantic 3d models of unstructured warehouses from coloured point clouds. Several scans from different locations of a laser scanner are integrated into a unique 3d dataset that is afterwards processed. The paper presents an efficient 3d processing algorithm that is able to segment, recognize and locate the existing materials of a storage place. The obtained 3d model provides to logistic managers precise and valuable information, such as: the current location of the stock, the free space for coming merchandises or the occupied volume variations between two scanning sessions taken at different times. The method has been tested under noise conditions in simulated scenarios and the extracted model has been compared with a ground truth model. The good results demonstrate that this approach could be useful in the logistic field.

关键词： 3d data processing Laser scanner 3d models Logistic 3d recognition and pose

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