Recognition of Planar Segments in Point Cloud Based on Wavelet Transform

Within industrial automation systems, three-dimensional (3-D) vision provides very useful feedback information in autonomous operation of various manufacturing equipment (e.g., industrial robots, material handling devices, assembly systems, and machine tools). The hardware performance in contemporary 3-D scanning devices is suitable for online utilization. However, the bottleneck is the lack of real-time algorithms for recognition of geometric primitives (e.g., planes and natural quadrics) from a scanned point cloud. One of the most important and the most frequent geometric primitive in various engineering tasks is plane. In this paper, we propose a new fast one-pass algorithm for recognition (segmentation and fitting) of planar segments from a point cloud. To effectively segment planar regions, we exploit the orthonormality of certain wavelets to polynomial function, as well as their sensitivity to abrupt changes. After segmentation of planar regions, we estimate the parameters of corresponding planes using standard fitting procedures. For point cloud structuring, a z-buffer algorithm with mesh triangles representation in barycentric coordinates is employed. The proposed recognition method is tested and experimentally validated in several real-world case studies

Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud

The expansion of the railway industry has increased the demand for the three-dimensional modeling of railway tracks. Due to the increasing development of UAV technology and its application advantages, in this research, the detection and 3D modeling of rail tracks are investigated using dense point clouds obtained from UAV images. Accordingly, a projection-based approach based on the overall direction of the rail track is proposed in order to generate a 3D model of the railway. In order to extract the railway lines, the height jump of points is evaluated in the neighborhood to select the candidate points of rail tracks. Then, using the RANSAC algorithm, line fitting on these candidate points is performed, and the final points related to the rail are identified. In the next step, the pre-specified rail piece model is fitted to the rail points through a projection-based process, and the orientation parameters of the model are determined. These parameters are later improved by fitting the Fourier curve, and finally a continuous 3D model for all of the rail tracks is created. The geometric distance of the final model from rail points is calculated in order to evaluate the modeling accuracy. Moreover, the performance of the proposed method is compared with another approach. A median distance of about 3 cm between the produced model and corresponding point cloud proves the high quality of the proposed 3D modeling algorithm in this study

GEOSPATIAL DATA PROCESSING FOR 3D CITY MODEL GENERATION, MANAGEMENT AND VISUALIZATION

Recent developments of 3D technologies and tools have increased availability and relevance of 3D data (from 3D points to complete city models) in the geospatial and geo-information domains. Nevertheless, the potential of 3D data is still underexploited and mainly confined to visualization purposes. Therefore, the major challenge today is to create automatic procedures that make best use of available technologies and data for the benefits and needs of public administrations (PA) and national mapping agencies (NMA) involved in “smart city” applications. The paper aims to demonstrate a step forward in this process by presenting the results of the SENECA project (Smart and SustaiNablE City from Above – http://seneca.fbk.eu). State-of-the-art processing solutions are investigated in order to (i) efficiently exploit the photogrammetric workflow (aerial triangulation and dense image matching), (ii) derive topologically and geometrically accurate 3D geo-objects (i.e. building models) at various levels of detail and (iii) link geometries with non-spatial information within a 3D geo-database management system accessible via web-based client. The developed methodology is tested on two case studies, i.e. the cities of Trento (Italy) and Graz (Austria). Both spatial (i.e. nadir and oblique imagery) and non-spatial (i.e. cadastral information and building energy consumptions) data are collected and used as input for the project workflow, starting from 3D geometry capture and modelling in urban scenarios to geometry enrichment and management within a dedicated webGIS platform

Automatic 3D City Modeling Using a Digital Map and Panoramic Images from a Mobile Mapping System

Three-dimensional city models are becoming a valuable resource because of their close geospatial, geometrical, and visual relationship with the physical world. However, ground-oriented applications in virtual reality, 3D navigation, and civil engineering require a novel modeling approach, because the existing large-scale 3D city modeling methods do not provide rich visual information at ground level. This paper proposes a new framework for generating 3D city models that satisfy both the visual and the physical requirements for ground-oriented virtual reality applications. To ensure its usability, the framework must be cost-effective and allow for automated creation. To achieve these goals, we leverage a mobile mapping system that automatically gathers high-resolution images and supplements sensor information such as the position and direction of the captured images. To resolve problems stemming from sensor noise and occlusions, we develop a fusion technique to incorporate digital map data. This paper describes the major processes of the overall framework and the proposed techniques for each step and presents experimental results from a comparison with an existing 3D city model

Low-rank Based Algorithms for Rectification, Repetition Detection and De-noising in Urban Images

In this thesis, we aim to solve the problem of automatic image rectification and repeated patterns detection on 2D urban images, using novel low-rank based techniques. Repeated patterns (such as windows, tiles, balconies and doors) are prominent and significant features in urban scenes. Detection of the periodic structures is useful in many applications such as photorealistic 3D reconstruction, 2D-to-3D alignment, facade parsing, city modeling, classification, navigation, visualization in 3D map environments, shape completion, cinematography and 3D games. However both of the image rectification and repeated patterns detection problems are challenging due to scene occlusions, varying illumination, pose variation and sensor noise. Therefore, detection of these repeated patterns becomes very important for city scene analysis. Given a 2D image of urban scene, we automatically rectify a facade image and extract facade textures first. Based on the rectified facade texture, we exploit novel algorithms that extract repeated patterns by using Kronecker product based modeling that is based on a solid theoretical foundation. We have tested our algorithms in a large set of images, which includes building facades from Paris, Hong Kong and New York

3D Building Synthesis Based on Images and Affine Invariant Salient Features

In this thesis, we introduce a method to synthesize and recognize buildings using a set of at least two 2D images taken from different views. Based on a coarse set of affine invariant salient feature points (corner points) on the images, a 3D high-resolution building model is obtained in accordance with the observed images. Corresponding salient points are found using the ratio of triangle areas formed from a set of four consecutive ordered salient corresponding points that form two triangles. The order is obtained by finding the vertices of the convex hull of the salient points. The salient points are tessellated to form a high-resolution triangular mesh with the appearance of a triangular patch in the image imported onto the personalized 3D model. With multiple images, all coordinates and appearances are reconstructed in accordance with the observed images. The 3D model reconstruction method allows for a 3D classification of a test building to one of many possible buildings stored in the database. The classification is based on a geometric 3D point cloud error. For buildings with very close 3D point cloud errors, a further classification is achieved based on the mean squared error (MSE) on the appearance of corresponding points on the test and base models. Our method can also be used in localization when preloaded location information of each model in the database is stored, hence helping an observer navigate without a GPS system.M.S., Electrical Engineering -- Drexel University, 201

CityGML rakennusmallien tuottaminen ilmalaserkeilauksesta

3D city models have become an important tool in many applications across different fields. Usually these 3D city models only represent the geometrical attributes of the city, which enables easy visualization of cities. Yet, different thematic queries, analysis tasks, and spatial data mining are out of the reach of models that only offer us information about their geometry. CityGML 3D city models bring an addition of semantic information to the models. In this thesis, the process and different techniques of building reconstruction from airborne laser scanning are explained. CityGML standard will also be explained and what has to be done in order to go from 3D building models to CityGML. The main focus of this thesis was to study how well it is possible to automatically create CityGML 2.0 3D city models from data collected only by airborne laser scanning. CityGML has five different levels-of-detail indicating the level of precision of the building. LOD1 and LOD2 were the most important levels for this thesis, and so it was tested how well different software were able to export reconstructed building models in the CityGML format with these precision levels. These exports were checked against the official specification of CityGML to see how well they met the requirements. It was also explained what more would be needed for the process and data, in order to produce higher quality models in LOD3. Two different test areas were chosen with different building and roof types. One area included detached houses, some partly covered with vegetation, and another area included mainly apartment houses. The thesis shows that as of now, it is still quite challenging to automatically produce city models that are in line with the CityGML 2.0 standard. The model driven methods had problems when it came to building installations, such as chimneys. These could not be modelled with software that used model driven methods. Data driven methods on the other hand had problems when it came to the conversion from the building models to the CityGML format. Terrain and terrain intersection curve also turned out to be more difficult to model than anticipated. Most of the software used in this thesis were not able to automatically handle the addition of these elements. The elements were possible to add later on to the CityGML file but only with use of additional software tools.3D kaupunkimalleista on tullut tärkeä työkalu eri alojen käyttämissä sovelluksissa. Yleensä näitä 3D kaupunkimalleja käytetään vain kaupunkien geometristen attribuuttien mallintamiseen visualisointitarkoituksiin. Kuitenkin erilaiset temaattiset kyselyt, analyysitehtävät ja spatiaalinen tiedonlouhinta ovat pelkästään geometriaa esittävien mallien ulottumattomissa. CityGML 3D kaupunkimallit ottavat huomioon lisäksi myös semanttisen tiedon. Tässä työssä selitetään rakennusten rekonstruointiprosessi ilmalaserkeilauksesta sekä esitellään erilaisia rekonstruointitekniikoita. Myös CityGML standardi esitellään sekä se, mitä 3D rakennusmalleille pitää tehdä, jotta ne saataisiin CityGML muotoon. Tämän työn pääpiste oli, kuinka hyvin on mahdollista automaattisesti luoda CityGML 2.0 muotoisia 3D kaupunkimalleja pelkästään ilmalaserkeilaamalla kerätystä aineistosta. CityGMLssä on viisi erilaista yksityiskohtatasoa, jotka kertovat, kuinka tarkasti rakennus on mallinnettu. Näistä LOD1 ja LOD2 olivat tämän työn kannalta oleellisimmat. Tämän vuoksi sitä, kuinka hyvin ohjelmista saadaan ulos rakennusmalleja CityGML muodossa näillä tarkkuusvaatimuksilla, testattiin. Saatuja tuloksia verrattiin virallisiin CityGML vaatimuksiin, jotta saatiin selville, kuinka hyvin vaatimukset täyttyivät. Myös se käytiin läpi, mitä muutoksia tarvittaisiin, jotta malleista saataisiin korkeamman, LOD3, tason malleja. Valittiin kaksi erilaista testialuetta, joilla oli erilaisia rakennus- ja kattotyyppejä. Toisella alueella oli omakotitaloja, joista jotkut olivat osittain kasvillisuuden peittämiä ja toisella oli pääsääntöisesti kerrostaloja. Työstä käy ilmi, että vielä tällä hetkellä automaattinen CityGML 2.0 standardin mukaisten kaupunkimallien tuottaminen on haastavaa. Mallipohjaisilla menetelmillä oli vaikeuksia rakennusten pienien osien, kuten savupiippujen, suhteen. Näitä ei pystytty mallintamaan ohjelmilla, jotka pohjautuivat mallipohjaisiin menetelmiin. Toisaalta tietopohjaisilla menetelmillä oli ongelmia, kun ne muunnettiin CityGML formaattiin. Maanpinnan sekä maanpinnan ja rakennuksen leikkauksen mallintamisessa oli odotettua enemmän ongelmia. Useimmat tässä työssä käytetyt ohjelmat eivät pystyneet automaattisesti näitä mallintamaan. Kuitenkin, ne oli mahdollista lisätä jälkikäteen käyttämällä muita ohjelmia