Reality-based 3D documentation of natural and cultural heritage sites—techniques, problems, and examples
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The importance of cultural and natural heritage documentation is well recognized at international level, and there is an increasing pressure to document and preserve heritage also digitally. The continuous development of new sensors, data capture methodologies, and multi-resolution 3D representations and the improvement of existing ones can contribute significantly to the 3D documentation, conservation, and digital presentation of heritages and to the growth of the research in this field. The article reviews some important documentation requirements and specifications, the actual 3D surveying and modeling techniques and methodologies with their limitations and potentialities as well some visualization issues involved in the heritage field. Some examples of world heritage sites 3D documentation are reported and discussed.
KeywordsPhotogrammetry Laser scanning Multi-resolution Multi-sensor Heritage sites
The heritage sites in the world (natural, cultural, or mixed) suffer from wars, natural disasters, weather changes, and human negligence. According to UNESCO, a heritage can be seen as an arch between what we inherit and what we leave behind. In the last years, great efforts focused on what we inherit as cultural heritage and on their documentation, in particular for visual man-made or natural heritages, which received a lot of attention and benefits from sensor and imaging advances. The importance of cultural heritage documentation is well recognized, and there is an increasing pressure to document and preserve them also digitally. Therefore, 3D data are nowadays a critical component to permanently record the shapes of important objects so that they might be passed down to future generations. This has produced firstly a large number of projects, mainly led by research groups, which have realized very good quality and complete digital models (Levoy et al. 2000; Beraldin et al. 2002; Stumpfel et al. 2003; Guidi et al. 2004; Gruen et al. 2004; Ikeuchi et al. 2007; El-Hakim et al. 2008; Guidi et al. 2009a; Remondino et al. 2009a) and secondly has alerted the creation of guidelines describing standards for correct and complete documentations.
Reality-based 3D modeling
Recording and processing of a large amount of 3D (possibly 4D) multi-source, multi-resolution, and multi-content information
Management and conservation of the achieved 3D (4D) models for further applications
Visualization and presentation of the results to distribute the information to other users allowing data retrieval through the Internet or advanced online databases
Digital inventories and sharing for education, research, conservation, entertainment, walkthrough, or tourism purposes
The continuous development of new sensors, data capture methodologies, multi-resolution 3D representations, and the improvement of existing ones are contributing significantly to the documentation, conservation, and presentation of heritage information and to the growth of research in the cultural heritage field. This is also driven by the increasing requests and needs for digital documentation of archaeological sites at different scales and resolutions.
Accuracy: Precision and reliability are two important factors of the surveying work, unless the work is done for simple and quick visualization.
Portability: The technique for terrestrial acquisitions should be portable due to accessibility problem of many sites, absence of electricity, location constraints, etc.
Low cost: Most archaeological and documentation missions have limited budgets, and they cannot effort expensive surveying instruments.
Fast acquisition: Most sites or excavation areas have limited time for documentation not to disturb works or visitors.
Flexibility: Due to the great variety and dimensions of sites and objects, the technique should allow different scales and it should be applicable in any possible condition.
3D range sensors
Optical range sensors (Blais 2004; Vosselman and Maas 2010) like pulsed, phase-shift, triangulation-based laser scanners, or stripe projection systems have received in the last years a great attention, also from non-experts, for 3D documentation and modeling purposes. Range sensors deliver directly ranges (i.e., distances thus 3D information in form of unstructured point clouds) and are getting quite common in the heritage field, despite their high costs, weight and the usual lack of good texture. During the surveying, the instrument should be placed in different locations or the object needs to be moved in a way that the instrument can see it under different viewpoints. Successively, the 3D raw data need errors and outliers removal, noise reduction, and sometimes holes filling before the alignment or registration of the data into a unique reference system is performed in order to produce a single point cloud of the surveyed scene or object. The registration is generally done in two steps: (a) manual or automatic raw alignment using targets or the data itself and (b) final global alignment based on iterative closest points (Salvi et al. 2007) or least squares method procedures (Gruen and Akca 2005). After the global alignment, redundant points should be removed before a surface model is produced and textured. The range-based modeling pipeline is quite straightforward, and many commercial or open source packages are available (Cignoni and Scopigno 2008).
Intrinsic characteristics of the instrument (calibration, measurement principle, etc.)
Characteristics of the scanned material in terms of reflection, light diffusion, and absorption (amplitude response)
Characteristics of the working environment
Coherence of the backscattered light (phase randomization)
Dependence from the chromatic content of the scanned material (frequency response)
Terrestrial range sensors works from very short ranges (few centimeters) up to few kilometers, in accordance with surface proprieties and environment characteristics, delivering 3D data with positioning accuracy from some hundreds of microns up to some millimeters. Range sensors, coupled with GPS/INS sensors, can also be used on airborne platforms (generally called LiDAR or airborne laser scanning; Shan and Toth 2008), mainly for digital terrain model (DTM)/digital surface model (DSM) generation and city modeling. LiDAR data are generally representing a DSM; therefore, for many applications, a filtering and reduction is required to obtain a DTM.
The main research issues involved in range-based data processing and modeling are the automated extraction of features (like man-made objects) and the automated generation of structured 3D data from the recorded 3D point clouds.
3D information could also be derived from a single image using object constraints (Van den Heuvel 1998; Criminisi et al. 1999; El-Hakim 2000) or estimating surface normals instead image correspondences (shape from shading (Horn and Brooks 1989), shape from texture (Kender 1978), shape from specularity (Healey and Binford 1987), shape from contour (Meyers et al. 1992), and shape from 2D edge gradients (Winkelbach and Wahl 2001)).
Many authors (Pomaska 2001; D’Ayala and Smars 2003; Heritage 2005) reported how the photogrammetric image-based approach allows surveys at different levels and in all possible combinations of object complexities, with high quality requirements, easy usage and manipulation of the final products, few time restrictions, good flexibility, and low costs. Different comparisons between photogrammetry and range sensors were also presented in the literature (Böhler 2005; Remondino et al. 2005; Grussenmeyer et al. 2008).
Multi-sensor and multi-source data integration
As previously mentioned, nowadays, the state-of-the-art approach for the 3D documentation and modeling of large and complex sites uses and integrates multiple sensors and technologies (photogrammetry, laser scanning, topographic surveying, etc.) to (a) exploit the intrinsic potentials and advantages of each technique, (b) compensate for the individual weaknesses of each method alone, (c) derive different geometric levels of detail (LOD) of the scene under investigation, and (d) achieve more accurate and complete geometric surveying for modeling, interpretation, representation, and digital conservation issues. 3D modeling based on multi-scale data and multi-sensors integration is indeed providing the best 3D results in terms of appearance and geometric detail. Each LOD is showing only the necessary information while each technique is used where best suited.
Since the 1990s, multiple data sources were integrated for industrial, military, and mobile mapping applications. Sensor and data fusion were then applied also in the cultural heritage domain, mainly at terrestrial level (Stumpfel et al. 2003; El-Hakim et al. 2004), although some projects mixed and integrated satellite, aerial, and ground information for a more complete and multi-resolution 3D survey (Gruen et al. 2005; Rönholm et al. 2007; Guidi et al. 2009a).
Beside images acquired in the visible part of the light spectrum, it is often necessary to acquire extra information provided by other sensors working in different spectral bands (e.g., IR, UV) in order to study deeper the object. Thermal infrared information is useful to analyze historical buildings, their state of conservation, reveal padding, older layers, back structure of frescoes while near IR is used to study paintings, revealing pentimenti, and preparatory drawings. On the other hand, the UV radiations are very useful in heritage studies to identify different varnishes and over-paintings, in particular with induced visible fluorescence imaging systems (Pelagotti et al. 2006). All those multi-modal information need to be aligned and often overlapped to the geometric data for information fusion, multi- spectral analysis, or other diagnostic applications (Remondino et al. 2009b).
Standards in digital 3D documentation
Many image-based modeling packages as well as range-based systems came out on the market in the last decades to allow the digital documentation and 3D modeling of objects or scenes. Many new users are approaching these methodologies, and those who are not really familiar with them need clear statements and information to know if a package or system satisfies certain requirements before investing. Therefore, technical standards for the 3D imaging field must be created, like those available for the traditional surveying or CMM. A part from standards, comparative data, and best practices are also needed, to show not only advantages but also limitations of systems and software. In these respects, the German VDI/VDE 2634 contains acceptance testing and monitoring procedures for evaluating the accuracy of close-range optical 3D vision systems (particularly for full-frame range cameras and single scan). The American Society for Testing and Materials with its E57 standards committee is trying to develop standards for 3D imaging systems for applications like surveying, preservation, construction, etc. The International Association for Pattern Recognition (IAPR) created the Technical Committee 19—Computer Vision for Cultural Heritage Applications—with the goal of promoting Computer Vision Applications in Cultural Heritage and their integration in all aspects of IAPR activities. TC19 aims at stimulating the development of components (both hardware and software) that can be used by researchers in cultural heritage like archaeologists, art historians, curators, and institutions like universities, museums, and research organizations.
As far as the presentation and visualization of the achieved 3D models concerns, the London Charter (http://www.londoncharter.org/) is seeking to define the basic objectives and principles for the use of 3D visualization methods in relation to intellectual integrity, reliability, transparency, documentation, standards, sustainability, and access of cultural heritage.
The Open Geospatial Consortium (OGC) developed the GML3, an extensible international standard for spatial data exchange. GML3 and other OGC standards (mainly the OpenGIS Web Feature Service Specification) provide a framework for exchanging simple and complex 3D models. Based on the GML3, the CityGML standard was created, an open data model and XML-based format for storing, exchanging, and representing 3D urban objects and in particular virtual city models.
Problems and bottlenecks
The actual problems and main challenges in the 3D surveying of large and complex sites or objects arise in every phase, from the data acquisition to the visualization of the achieved 3D results. The actual great challenges lie in selecting the appropriate methodology (sensor, hardware, software), the appropriate data processing procedure, designing the production workflow, and assuring that the final result is in accordance with all the given technical specifications and being able to fluently display and interact with the achieved 3D model.
Reality-based surveying and 3D modeling is highly dependent on the quality of the acquired or available data.
The huge amount of (range) data makes very time-consuming and difficult their processing at high resolution, yet processing at low resolution creates accuracy problems and a possible lose of geometric details.
Combining data acquired with different sensors, at different geometric resolution, and under different viewpoints can affect the overall accuracy of the entire 3D model if not properly considered and afterward merged.
Despite combining several sensors, some gaps and holes can still be present in the produced 3D model, requiring filled and interpolated surface patches not to leave them visible and unpleasant.
The used sampled distance in scanning is rarely optimal for the entire site or object, producing under-sampled regions where edges and high curvature surfaces are present and over-sampled regions where flat areas are.
In case of satellite and aerial images, the availability of the data could be a problem due to weather conditions or restrictions on flights. For terrestrial acquisition, size, location, and surface (geometry and material) of the object or site can create several problems. The dimensions and accessibility problems (due to location, obstructions, rough or sloped terrain with stones, rocks and holes, unfavorable weather conditions, etc.) can cause delays, occlusions, and can result in missing sections or enforce wide-baseline images and poor geometric configurations. The complexity of some parts can create self-occlusions or holes in the coverage, in addition to the occlusions from plants, trees, restoration scaffolds, or tourists. The absence of high platforms for a higher location of the data acquisition might cause missing parts, e.g., for the roofs.
For active sensors, the object material (e.g., marble) has often an important influence on the acquired data since it can cause penetration (Godin et al. 2001; Lichti and Harvey 2002; Guidi et al. 2009b) or bad reflection effects. Moreover, transportability and usability problems arise in certain field campaigns located in remote areas.
Data processing and point cloud generation
For image-based approaches, terrestrial digital cameras must be accurately calibrated, preferably in a controlled lab environment, with a 3D testfield and a bundle adjustment solution with additional parameters to fully compensate for systematic errors (Remondino and Fraser 2006). As no commercial procedure is readily available for automated markerless tie point extraction from terrestrial convergent images, the image orientation phase is still highly interactive, although some recent works seem to be promising in terms of both accuracy and automation (Barazzetti et al. 2009). In case of aerial and satellite imagery, more automation is present in the data processing, although the control points still need to be measured manually. For the surface measurement, manual and semi-automated measurements are still much more reliable in particular for complex architectural scenes or man-made objects. For small free-form objects or ornaments rich of details, dense matching techniques can be instead applied to derive dense 3D point clouds (Remondino et al. 2008).
As far as range-based approaches concerns, the first operations performed on the acquired data are possible errors and outliers removal, noise reduction, and holes filling (Weyrich et al. 2004), followed by the alignment (or registration) of the multiple scans (Salvi et al. 2007). The registration phase is quite straightforward although the identification of homologous points between the overlapping point clouds is still fully interactive unless some targets are placed in the surveyed scene.
Once a point cloud (i.e., unstructured data) is available, a polygonal model (i.e., structured data) needs to be generated to produce the best digital representation of the surveyed object or scene. For architectural scenes and objects, generally described with sparse point clouds, a segmentation and structuring phase is necessary before producing a mesh model. Dense point clouds derived with automated image matching methods or measured with range sensors can be directly converted into meshes, following some possible editing and cleaning. Then some repairing to close holes, fix incorrect faces, or non-manifold parts are often demanding (and time-consuming). Those errors are visually unpleasant, might cause lighting blemishes due to the incorrect normals and the computer model will also be unsuitable for reverse engineering or physical replicas. Moreover, over-sampled areas should be simplified while under-sampled regions should be subdivided. Finally, photo-realism, defined as having no difference between a view rendered from the model and a photograph taken from the same viewpoint, is generally required and achieved with the texture mapping phase, e.g., projecting one or more images (or orthophotos) onto the 3D geometry. Generally, problems might rise from the time-consuming image-to-geometry registration or because of variations in lighting, surface specularity, and camera settings. Often the images are exposed with the illumination at imaging time, but it may need to be replaced by illumination consistent with the rendering point of view and the reflectance properties (bidirectional reflectance distribution function) of the object (Lensch et al. 2003). High dynamic range (HDR) images might also be acquired to recover all scene details (Reinhard et al. 2005) while color discontinuities and aliasing effects must be removed (Debevec et al. 2004; Umeda et al. 2005; Callieri et al. 2008).
Realistic visualization of the 3D results
The ability to easily interact with a huge 3D model is a continuing and increasing problem, in particular with the new demand of sharing and offering online and real-time visualizations. Indeed, model sizes (both in geometry and texture) are increasing at faster rate than computer hardware and software advances, and this limits the possibilities for interactive and real-time visualization of the 3D results. Due to the generally large amount of data and its complexity, the rendering of large 3D models is done with a multi-resolution approach displaying large textured meshes with different levels of detail and simplification approaches (Luebke et al. 2002; Cignoni et al. 2005; Dietrich et al. 2007).
The Etruscan necropolis of Tarquinia (Italy)
Geometric data: a TOF scanner surveying acquired a large amount of range data for the exterior (ten stations @ 1 cm geometric resolution, ca. 2 Mil. points) and the underground interior rooms (13 stations @ 4 mm sampling step, ca. 14 Mil. points). After the geometric alignment and data reduction, a complete mesh was produced for further rendering, interactive visualization, and archaeological documentation purposes.
- Appearance data, constituted of:
Visible images for texturing purposes: For the photo-realistic rendering of the final 3D model, ca. 160 HDR textures were acquired with a 13.5 Mpixel Kodak DCS camera pre-calibrated in the lab at a focal length setting of 50 mm. A constant illumination in the underground rooms was achieved using cold neon lights (to avoid heating effects on the frescoes) and a spot-meter.
Multi-spectral images for diagnostics studies: On some selected areas, visible reflectance, IR reflectography, and UV-induced fluorescence images were acquired using interferential filters in front of a calibrated cooled CCD camera. Those images were afterward calibrated, registered, processed, and overlapped onto the 3D geometry to perform quantitative analysis and differentiate pigments, being present, hidden, or disappeared to the naked eye. Indeed, all the materials having the same color in a certain light have different chemical compositions and reflectance spectra and can therefore identified with multi-spectral imaging.
The integration between the different data sources was essential to overcome some limits of each method and to have as result the complete geometric and appearance information about materials and techniques used to build the heritage.
Pompeii and its Roman Forum
When Vesuvius erupted on 24 August A.D. 79, it engulfed the two flourishing Roman towns of Pompeii and Herculaneum, as well as the many wealthy villas in the area. These have been progressively excavated and made accessible to the public since the mid-nineteenth century. The vast expanse of the commercial town of Pompeii contrasts with the smaller but better-preserved remains of the holiday resort of Herculaneum, while the superb wall paintings of the Villa Oplontis at Torre Annunziata give a vivid impression of the opulent lifestyle enjoyed by the wealthier citizens of the Early Roman Empire (http://whc.unesco.org).
The entire 3D model of the forum was afterward linked to the existing superintendence archaeological databases (Fig. 9c). The relationship database–3D model was implemented in two ways: (a) from the geometrical 3D data to the archaeological 2D data, for explaining historical and conservation details of a specific artifact in the forum, and (b) from a specific document or philological detail to its corresponding location in the 3D space (Manferdini et al. 2008).
Laces’s prehistorical stela (Italy)
Conclusions and outlook
Despite the fact that the 3D documentation is not yet the state-of-the-art in the heritage field, the reported examples show the potentialities of the modern surveying technologies to digitally document and preserve our heritages as well as share and manage them. But it is clear that the image-based 3D documentation approach, together with active optical sensors, spatial information systems, 3D modeling procedures, visualization, and animation software, is still all in a dynamic state of development, with even better application prospects for the near future.
The authors are really thankful to Prof. A. Gruen and Dr. M. Sauerbier (ETH Zurich, Switzerland), Prof. G. Guidi and Dr. M. Russo (Politecnico of Milan, Italy), Dr. S. El-Hakim and Dr. A.J. Beraldin (NRC Canada), S. Girardi (FBK Trento, Italy), S. Benedetti (Benedetti Graphics), A. Pelagotti (INOA-CNR, Italy), Dr. L. Marras (Art-Test, Italy), Dr. S. Campana and M. Sordini (University of Siena, Italy), Dr. B. Benedetti (SNS Pisa, Italy), and Dr. A.M. Manferdini and Prof. M. Gaiani (Univ. of Bologna, Italy) co-authors in some publications, researches, and field works for the reported projects and examples.
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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