3D mapping of underground environments with a hand-held laser scanner

The development of several instruments and techniques for reality-based 3D survey provides for new effective and affordable solutions for mapping underground environments. Terrestrial laser scanning (TLS) techniques demonstrated to be suitable for recording complex surfaces in high resolution even in low ambient lightning conditions. TLS approaches allow to obtain millions of 3D points and very detailed representations of complex environments, but these normally required a very high number of stations. This paper presents the investigation and deployment of a hand-held laser scanning system, the GeoSlam Zeb1, for the fast 3D digitization of underground tunnels. This active hand-held device was employed in two different typologies of underground structures: the Grotta di Seiano (Fig.1 a-b), a 800 m long monumental passage used as entrance of a roman villa in Posillipo (Naples), and some military fortifications (Fig.1 c-d) built during the First World War (WWI) on the hills around Trento. In the first case study, owing to the length of the gallery and the lack of well-defined geometric features on its wall, errors in the alignment were expected. Consequently, the final alignment of the numerous acquired scans was verified. In the second part, the research is focused on suitable procedures for the final three-dimensional representation and visualization of complex underground passages, i.e. the military tunnels. Using an automatic classification procedure on the point-clouds, vegetation was removed and, through a manual segmentation approach, the rooms were classified according to their specific functions. In the paper, the results are critical presented and discussed

Lo spazio virtuale del passato. Documentazione, modellazione e fruizione virtuale del patrimonio archeologico. Il Parco Archeologico di Pausilypon.

L’archeologia, fondata su una cultura materiale da registrare, interpretare e trasmettere, è una disciplina essenzialmente visuale. La nascita di nuovi processi di produzione delle immagini rende il tema della visualizzazione archeologica, in particolare, sempre più centrale. L’introduzione delle tecnologie digitali nel campo del patrimonio culturale ha segnato, infatti, l’inizio di un processo di “virtualizzazione” della conoscenza e della conservazione dei beni. Nei sistemi di produzione della conoscenza della “Virtual and Cyber-Era”, l’immagine archeologica trova così una sua nuova dimensione, digitale, tridimensionale, poli-funzionale, virtuale ed interattiva. La tesi presenta il lavoro di documentazione e modellazione tridimensionale condotto nel Parco Archeologico di Pausilypon, presentando lo sviluppo di tour virtuali per la fruizione virtuale dei modelli digitali

Sparse point cloud filtering based on covariance features

This work presents an extended photogrammetric pipeline aimed to improve 3D reconstruction results. Standard photogrammetric pipelines can produce noisy 3D data, especially when images are acquired with various sensors featuring different properties. In this paper, we propose an automatic filtering procedure based on some geometric features computed on the sparse point cloud created within the bundle adjustment phase. Bad 3D tie points and outliers are detected and removed, relying on micro and macro-clusters analyses. Clusters are built according to the prevalent dimensionality class (1D, 2D, 3D) assigned to low-entropy points, and corresponding to the main linear, planar o scatter local behaviour of the point cloud. While the macro-clusters analysis removes smallsized clusters and high-entropy points, in the micro-clusters investigation covariance features are used to verify the inner coherence of each point to the assigned class. Results on heritage scenarios are presented and discussed

3D virtualization of an underground semi-submerged cave system

Underwater caves represent the most challenging scenario for exploration, mapping and 3D modelling. In such complex environment, unsuitable to humans, highly specialized skills and expensive equipment are normally required. Technological progress and scientific innovation attempt, nowadays, to develop safer and more automatic approaches for the virtualization of these complex and not easily accessible environments, which constitute a unique natural, biological and cultural heritage. This paper presents a pilot study realised for the virtualization of 'Grotta Giusti' (Fig. 1), an underground semi-submerged cave system in central Italy. After an introduction on the virtualization process in the cultural heritage domain and a review of techniques and experiences for the virtualization of underground and submerged environments, the paper will focus on the employed virtualization techniques. In particular, the developed approach to simultaneously survey the semi-submersed areas of the cave relying on a stereo camera system and the virtualization of the virtual cave will be discussed

Digitizing Historical Aerial Images: Evaluation of the Effects of Scanning Quality on Aerial Triangulation and Dense Image Matching

In the last decade, many aerial photographic archives have started to be digitized for multiple purposes, including digital preservation and geoprocessing. This paper analyzes the effects of professional photogrammetric versus consumer-grade scanners on the processing of analog historical aerial photographs. An image block over Warsaw is considered, featuring 38 photographs acquired in 1986 (Wild RC10, Normal Aviogon II lens, 23 × 23 cm format) with a ground sampling distance (GSD) of 4 cm. Aerial triangulation (AT) and dense image matching (DIM) procedures are considered, analyzing how scanning modalities are important in the massive digitization of analog images for georeferencing and 3D product generation. The achieved results show how consumer-grade scanners, unlike more expensive photogrammetric scanners, do not possess adequate recording quality to ensure high accuracy and geometric precision for geoprocessing purposes. However, consumer-grade scanners can be used for time and cost-efficient applications where a partial loss of data quality is not critical

4D Building Reconstruction with Machine Learning and Historical Maps

open3siThe increasing importance of three-dimensional (3D) city modelling is linked to these data’s different applications and advantages in many domains. Images and Light Detection and Ranging (LiDAR) data availability are now an evident and unavoidable prerequisite, not always verified for past scenarios. Indeed, historical maps are often the only source of information when dealing with historical scenarios or multi-temporal (4D) digital representations. The paper presents a methodology to derive 4D building models in the level of detail 1 (LoD1), inferring missing height information through machine learning techniques. The aim is to realise 4D LoD1 buildings for geospatial analyses and visualisation, valorising historical data, and urban studies. Several machine learning regression techniques are analysed and employed for deriving missing height data from digitised multi-temporal maps. The implemented method relies on geometric, neighbours, and categorical attributes for height prediction. Derived elevation data are then used for 4D building reconstructions, offering multitemporal versions of the considered urban scenarios. Various evaluation metrics are also presented for tackling the common issue of lack of ground-truth information within historical data.openFarella, Elisa Mariarosaria; Özdemir, Emre; Remondino, FabioFarella, Elisa Mariarosaria; Özdemir, Emre; Remondino, Fabi

Colorizing the Past: Deep Learning for the Automatic Colorization of Historical Aerial Images

The colorization of grayscale images can, nowadays, take advantage of recent progress and the automation of deep-learning techniques. From the media industry to medical or geospatial applications, image colorization is an attractive and investigated image processing practice, and it is also helpful for revitalizing historical photographs. After exploring some of the existing fully automatic learning methods, the article presents a new neural network architecture, Hyper-U-NET, which combines a U-NET-like architecture and HyperConnections to handle the colorization of historical black and white aerial images. The training dataset (about 10,000 colored aerial image patches) and the realized neural network are available on our GitHub page to boost further research investigations in this field

Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality Measures

The paper presents an ecient photogrammetric workflow to improve the 3D reconstruction of scenes surveyed by integrating terrestrial and Unmanned Aerial Vehicle (UAV) images. In the last years, the integration of this kind of images has shown clear advantages for the complete and detailed 3D representation of large and complex scenarios. Nevertheless, their photogrammetric integration often raises several issues in the image orientation and dense 3D reconstruction processes. Noisy and erroneous 3D reconstructions are the typical result of inaccurate orientation results. In this work, we propose an automatic filtering procedure which works at the sparse point cloud level and takes advantage of photogrammetric quality features. The filtering step removes low-quality 3D tie points before refining the image orientation in a new adjustment and generating the final dense point cloud. Our method generalizes to many datasets, as it employs statistical analyses of quality feature distributions to identify suitable filtering thresholds. Reported results show the eectiveness and reliability of the method verified using both internal and external quality checks, as well as visual qualitative comparisons. We made the filtering tool publicly available on GitHub

Uncertainty-Guided Depth Fusion from Multi-View Satellite Images to Improve the Accuracy in Large-Scale DSM Generation

The generation of digital surface models (DSMs) from multi-view high-resolution (VHR) satellite imagery has recently received a great attention due to the increasing availability of such space-based datasets. Existing production-level pipelines primarily adopt a multi-view stereo (MVS) paradigm, which exploit the statistical depth fusion of multiple DSMs generated from individual stereo pairs. To make this process scalable, these depth fusion methods often adopt simple approaches such as the median filter or its variants, which are efficient in computation but lack the flexibility to adapt to heterogenous information of individual pixels. These simple fusion approaches generally discard ancillary information produced by MVS algorithms (such as measurement confidence/uncertainty) that is otherwise extremely useful to enable adaptive fusion. To make use of such information, this paper proposes an efficient and scalable approach that incorporates the matching uncertainty to adaptively guide the fusion process. This seemingly straightforward idea has a higher-level advantage: first, the uncertainty information is obtained from global/semiglobal matching methods, which inherently populate global information of the scene, making the fusion process nonlocal. Secondly, these globally determined uncertainties are operated locally to achieve efficiency for processing large-sized images, making the method extremely practical to implement. The proposed method can exploit results from stereo pairs with small intersection angles to recover details for areas where dense buildings and narrow streets exist, but also to benefit from highly accurate 3D points generated in flat regions under large intersection angles. The proposed method was applied to DSMs generated from Worldview, GeoEye, and Pleiades stereo pairs covering a large area (400 km2 ). Experiments showed that we achieved an RMSE (root-mean-squared error) improvement of approximately 0.1–0.2 m over a typical Median Filter approach for fusion (equivalent to 5–10% of relative accuracy improvement)