1,720,982 research outputs found
Quality-based registration refinement of airborne LiDAR and photogrammetric point clouds
No full textA big challenge in geodata processing is the seamless and accurate integration of airborne LiDAR (Light Detection And Ranging) and photogrammetric point clouds performed by properly considering their high variations in resolution and precision. In this paper we propose a new approach to co-register airborne point clouds acquired by LiDAR sensors and photogrammetric algorithms, assuming that only dense point clouds from both mapping methods are available, without LiDAR raw data nor flight trajectories. First, semantically segmented point clouds are quality-wise evaluated by assigning sensor-specific quality features to each 3D point. Then, these quality features are aggregated in order to assign a score to each 3D point based on its quality. Finally, using a voxel-based structure, a filtering step is performed to select only the best points used for the registration refinement. We assess the performance of the proposed method on two different case studies to demonstrate its advantages compared to a traditional ICP-based approach. The code of the implemented method is available at https://github.com/3DOM-FBK/HyRe
Geometric feature analysis for the classification of cultural heritage point clouds
Full text linkIn the last years, the application of artificial intelligence (Machine Learning and Deep Learning methods) for the classification of 3D point clouds has become an important task in modern 3D documentation and modelling applications. The identification of proper geometric and radiometric features becomes fundamental to classify 2D/3D data correctly. While many studies have been conducted in the geospatial field, the cultural heritage sector is still partly unexplored. In this paper we analyse the efficacy of the geometric covariance features as a support for the classification of Cultural Heritage point clouds. To analyse the impact of the different features calculated on spherical neighbourhoods at various radius sizes, we present results obtained on four different heritage case studies using different features configurations
Di-segno, ricostruzione 3D e navigazione virtuale. Il racconto dell'utopia interrotta di Ferdinandopoli
No full textA San Leucio l'incompiuta città di Ferdinandopoli racconta due storie: le aspirazioni di un sovrano, Ferdinando IV, e la parziale realizzazione di un'utopia urbana. sfruttando le moderne metodologie
di rilievo fotogrammetrico digitale, lo studio ha permesso la realizzazione di un catalogo tridimensionale, metrico e colorimetrico, degli elementi ricorrenti nell' architettura costruita. Il confronto e l'integrazione tra i dati ottenuti e i risultati raggiunti dagli studi precedenti hanno fornito la base per la modellazione
30 del non costruito. l a ricostruzione virtuale è resa interattiva e navigabile, rendendo possibile la narrazione della storia incompiuta di un progetto illuminato
Un catalogo semantico per la conoscenza e la ricostruzione del paesaggio incompiuto. Il caso di San Leucio
No full textSan Leucio (Caserta) is one of the most interesting experiments in the economic, social and urban development of a Bourbon-era industrial city. The planning of the project under King Ferdinando IV is difficult to understand, given the loss of Collecini’s drawings and the failure to complete development of the utopian “Ferdinandopoli”. Nevertheless, the buildings actually constructed serve in illustrating architectural relationships, hierarchies and styles. These features make this project and its integration in the landscape one of the most important industrial experiments of the period. The current report derives from research by the universities of the Region of Campania. The first step is the acquisition of three-dimensional data on the built part of Ferdinandopoli, using digital photogrammetry. The second is the realization of a three-dimensional catalogue of the architectural components and elements, through their segmentation and matching in semantic families. This process serves in identifying recurring elements, useful for hypothetical reconstruction of the unrealized parts of this sit
NERFBK: A HOLISTIC DATASET FOR BENCHMARKING NERF-BASED 3D RECONSTRUCTION
Full text linkNeural Radiance Field methods are innovative solutions to derive 3D data from a set of oriented images. This paper introduces new real and synthetic image datasets - called NeRFBK - specifically designed for testing and comparing NeRF-based 3D reconstruction algorithms. More and more reconstruction algorithms and techniques are available nowadays, raising the need to evaluate and compare the quality of derived 3D products currently used in various domains and applications. However, gathering diverse data with precise ground truth is challenging and may not encompass all relevant applications. The NeRFBK dataset addresses this issue by providing multi-scale, indoor and outdoor datasets with high-resolution images and videos and camera parameters for testing and comparing NeRF-based algorithms. This paper presents the design and creation of the NeRFBK set of data, various examples and application scenarios, and highlights its potential for advancing the field of 3D reconstruction
NeRFBK: a holistic dataset for benchmarking NeRF-based 3D reconstruction
Full text linkNeural Radiance Field methods are innovative solutions to derive 3D data from a set of oriented images. This paper introduces new real and synthetic image datasets - called NeRFBK - specifically designed for testing and comparing NeRF-based 3D reconstruction algorithms. More and more reconstruction algorithms and techniques are available nowadays, raising the need to evaluate and compare the quality of derived 3D products currently used in various domains and applications. However, gathering diverse data with precise ground truth is challenging and may not encompass all relevant applications. The NeRFBK dataset addresses this issue by providing multi-scale, indoor and outdoor datasets with high-resolution images and videos and camera parameters for testing and comparing NeRF-based algorithms. This paper presents the design and creation of the NeRFBK set of data, various examples and application scenarios, and highlights its potential for advancing the field of 3D reconstruction
Nerf for heritage 3d reconstruction
Full text linkConventional or learning-based 3D reconstruction methods from images have clearly shown their potential for 3D heritage documentation. Nevertheless, Neural Radiance Field (NeRF) approaches are recently revolutionising the way a scene can be rendered or reconstructed in 3D from a set of oriented images. Therefore the paper wants to review some of the last NeRF methods applied to various cultural heritage datasets collected with smartphone videos, touristic approaches or reflex cameras. Firstly several NeRF methods are evaluated. It turned out that Instant-NGP and Nerfacto methods achieved the best outcomes, outperforming all other methods significantly. Successively qualitative and quantitative analyses are performed on various datasets, revealing the good performances of NeRF methods, in particular for areas with uniform texture or shining surfaces, as well as for small datasets of lost artefacts. This is for sure opening new frontiers for 3D documentation, visualization and communication purposes of digital heritage
From 3D surveying data to BIM to BEM: the InCUBE dataset
Full text linkIn recent years, the improvement of sensors and methodologies for 3D reality-based surveying has exponentially enhanced the possibility of creating digital replicas of the real world. LiDAR technologies and photogrammetry are currently standard approaches for collecting 3D geometric information of indoor and outdoor environments at different scales. This information can potentially be part of a broader processing workflow that, starting from 3D surveyed data and through Building Information Models (BIM) generation, leads to more complex analyses of buildings’ features and behavior (Figure 1). However, creating BIM models, especially of historic and heritage assets (HBIM), is still resource-intensive and time-consuming due to the manual efforts required for data creation and enrichment. Improve 3D data processing, interoperability, and the automation of the BIM generation process are some of the trending research topics, and benchmark datasets are extremely helpful in evaluating newly developed algorithms and methodologies for these scopes. This paper introduces the InCUBE dataset, resulting from the activities of the recently funded EU InCUBE project, focused on unlocking the EU building renovation through integrated strategies and processes for efficient built-environment management (including the use of innovative renewable energy technologies and digitalization). The set of data collects raw and processed data produced for the Italian demo site in the Santa Chiara district of Trento (Italy). The diversity of the shared data enables multiple possible uses, investigations and developments, and some of them are presented in this contribution
Unveiling large-scale historical contents with V-SLAM and markerless mobile AR solutions
Full text linkAugmented Reality (AR) is already transforming many fields, from medical applications to industry, entertainment and heritage. In its
most common form, AR expands reality with virtual 3D elements, providing users with an enhanced and enriched experience of the
surroundings. Until now, most of the research work focused on techniques based on markers or on GNSS/INS positioning. These
approaches require either the preparation of the scene or a strong satellite signal to work properly. In this paper, we investigate the use
of visual-based methods, i.e., methods that exploit distinctive features of the scene estimated with Visual Simultaneous Localization
and Mapping (V-SLAM) algorithms, to determine and track the user position and attitude. The detected features, which encode the
visual appearance of the scene, can be saved and later used to track the user in successive AR sessions. Existing AR frameworks like
Google ARCore, Apple ARKit and Unity AR Foundation recently introduced visual-based localization in their frameworks, but they
target mainly small scenarios. We propose a new Mobile Augmented Reality (MAR) methodology that exploits OPEN-V-SLAM to
extend the application range of Unity AR Foundation and better handle large-scale environments. The proposed methodology is
successfully tested in both controlled and real-case large heritage scenarios. Results are available also in this video:
https://youtu.be/Q7VybmiWIuI
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