A FLEXIBLE METHODOLOGY FOR OUTDOOR/INDOOR BUILDING RECONSTRUCTION FROM OCCLUDED POINT CLOUDS

Terrestrial Laser Scanning data are increasingly used in building survey not only in cultural heritage domain but also for as-built modelling of large and medium size civil structures. However, raw point clouds derived from laser scanning generally not directly ready for the generation of such models. A time-consuming manual modelling phase has to be taken into account. In addition the large presence of occlusion and clutter may turn out in low-quality building models when state-of-the-art automatic modelling procedures are applied. This paper presents an automated procedure to convert raw point clouds into semantically-enriched building models. The developed method mainly focuses on a geometrical complexity typical of modern buildings with clear prevalence of planar features A characteristic of this methodology is the possibility to work with outdoor and indoor building environments. In order to operate under severe occlusions and clutter a couple of completion algorithms were designed to generate a plausible and reliable model. Finally, some examples of the developed modelling procedure are presented and discussed

Interactive inspection of complex multi-object industrial assemblies

The final publication is available at Springer via http://dx.doi.org/10.1016/j.cad.2016.06.005The use of virtual prototypes and digital models containing thousands of individual objects is commonplace in complex industrial applications like the cooperative design of huge ships. Designers are interested in selecting and editing specific sets of objects during the interactive inspection sessions. This is however not supported by standard visualization systems for huge models. In this paper we discuss in detail the concept of rendering front in multiresolution trees, their properties and the algorithms that construct the hierarchy and efficiently render it, applied to very complex CAD models, so that the model structure and the identities of objects are preserved. We also propose an algorithm for the interactive inspection of huge models which uses a rendering budget and supports selection of individual objects and sets of objects, displacement of the selected objects and real-time collision detection during these displacements. Our solution–based on the analysis of several existing view-dependent visualization schemes–uses a Hybrid Multiresolution Tree that mixes layers of exact geometry, simplified models and impostors, together with a time-critical, view-dependent algorithm and a Constrained Front. The algorithm has been successfully tested in real industrial environments; the models involved are presented and discussed in the paper.Peer ReviewedPostprint (author's final draft

GraXML - Modular Geometric Modeler

Many entities managed by HEP Software Frameworks represent spatial (3-dimensional) real objects. Effective definition, manipulation and visualization of such objects is an indispensable functionality. GraXML is a modular Geometric Modeling toolkit capable of processing geometric data of various kinds (detector geometry, event geometry) from different sources and delivering them in ways suitable for further use. Geometric data are first modeled in one of the Generic Models. Those Models are then used to populate powerful Geometric Model based on the Java3D technology. While Java3D has been originally created just to provide visualization of 3D objects, its light weight and high functionality allow an effective reuse as a general geometric component. This is possible also thanks to a large overlap between graphical and general geometric functionality and modular design of Java3D itself. Its graphical functionalities also allow a natural visualization of all manipulated elements. All these techniques have been developed primarily (or only) for the Java environment. It is, however, possible to interface them transparently to Frameworks built in other languages, like for example C++. The GraXML toolkit has been tested with data from several sources, as for example ATLAS and ALICE detector description and ATLAS event data. Prototypes for other sources, like Geometry Description Markup Language (GDML) exist too and interface to any other source is easy to add.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003. PSN THJT00

Discovering Regularity in Point Clouds of Urban Scenes

Despite the apparent chaos of the urban environment, cities are actually replete with regularity. From the grid of streets laid out over the earth, to the lattice of windows thrown up into the sky, periodic regularity abounds in the urban scene. Just as salient, though less uniform, are the self-similar branching patterns of trees and vegetation that line streets and fill parks. We propose novel methods for discovering these regularities in 3D range scans acquired by a time-of-flight laser sensor. The applications of this regularity information are broad, and we present two original algorithms. The first exploits the efficiency of the Fourier transform for the real-time detection of periodicity in building facades. Periodic regularity is discovered online by doing a plane sweep across the scene and analyzing the frequency space of each column in the sweep. The simplicity and online nature of this algorithm allow it to be embedded in scanner hardware, making periodicity detection a built-in feature of future 3D cameras. We demonstrate the usefulness of periodicity in view registration, compression, segmentation, and facade reconstruction. The second algorithm leverages the hierarchical decomposition and locality in space of the wavelet transform to find stochastic parameters for procedural models that succinctly describe vegetation. These procedural models facilitate the generation of virtual worlds for architecture, gaming, and augmented reality. The self-similarity of vegetation can be inferred using multi-resolution analysis to discover the underlying branching patterns. We present a unified framework of these tools, enabling the modeling, transmission, and compression of high-resolution, accurate, and immersive 3D images

Joint segmentation of color and depth data based on splitting and merging driven by surface fitting

This paper proposes a segmentation scheme based on the joint usage of color and depth data together with a 3D surface estimation scheme. Firstly a set of multi-dimensional vectors is built from color, geometry and surface orientation information. Normalized cuts spectral clustering is then applied in order to recursively segment the scene in two parts thus obtaining an over-segmentation. This procedure is followed by a recursive merging stage where close segments belonging to the same object are joined together. At each step of both procedures a NURBS model is fitted on the computed segments and the accuracy of the fitting is used as a measure of the plausibility that a segment represents a single surface or object. By comparing the accuracy to the one at the previous step, it is possible to determine if each splitting or merging operation leads to a better scene representation and consequently whether to perform it or not. Experimental results show how the proposed method provides an accurate and reliable segmentation