scholarly journals INCREMENTAL REFINEMENT OF FAÇADE MODELS WITH ATTRIBUTE GRAMMAR FROM 3D POINT CLOUDS

2016 ◽  
Vol III-3 ◽  
pp. 311-316
Author(s):  
Y. Dehbi ◽  
C. Staat ◽  
L. Mandtler ◽  
L. Pl¨umer
Keyword(s):  
A Priori ◽  
Point Clouds ◽  
3D Point Clouds ◽  
Architectural Patterns ◽  
Grammar Rules ◽  
Probability Densities ◽  
Parse Trees ◽  
Incremental Interpretation ◽  
Context Free ◽  
Building Reconstruction

Data acquisition using unmanned aerial vehicles (UAVs) has gotten more and more attention over the last years. Especially in the field of building reconstruction the incremental interpretation of such data is a demanding task. In this context formal grammars play an important role for the top-down identification and reconstruction of building objects. Up to now, the available approaches expect offline data in order to parse an a-priori known grammar. For mapping on demand an on the fly reconstruction based on UAV data is required. An incremental interpretation of the data stream is inevitable. This paper presents an incremental parser of grammar rules for an automatic 3D building reconstruction. The parser enables a model refinement based on new observations with respect to a weighted attribute context-free grammar (WACFG). The falsification or rejection of hypotheses is supported as well. The parser can deal with and adapt available parse trees acquired from previous interpretations or predictions. Parse trees derived so far are updated in an iterative way using transformation rules. A diagnostic step searches for mismatches between current and new nodes. Prior knowledge on fac¸ades is incorporated. It is given by probability densities as well as architectural patterns. Since we cannot always assume normal distributions, the derivation of location and shape parameters of building objects is based on a kernel density estimation (KDE). While the level of detail is continuously improved, the geometrical, semantic and topological consistency is ensured.

scholarly journals INCREMENTAL REFINEMENT OF FAÇADE MODELS WITH ATTRIBUTE GRAMMAR FROM 3D POINT CLOUDS

2016 ◽  
Vol III-3 ◽  
pp. 311-316 ◽  
Author(s):  
Y. Dehbi ◽  
C. Staat ◽  
L. Mandtler ◽  
L. Pl¨umer
Keyword(s):  
A Priori ◽  
Point Clouds ◽  
3D Point Clouds ◽  
Architectural Patterns ◽  
Grammar Rules ◽  
Probability Densities ◽  
Parse Trees ◽  
Incremental Interpretation ◽  
Context Free ◽  
Building Reconstruction

Data acquisition using unmanned aerial vehicles (UAVs) has gotten more and more attention over the last years. Especially in the field of building reconstruction the incremental interpretation of such data is a demanding task. In this context formal grammars play an important role for the top-down identification and reconstruction of building objects. Up to now, the available approaches expect offline data in order to parse an a-priori known grammar. For mapping on demand an on the fly reconstruction based on UAV data is required. An incremental interpretation of the data stream is inevitable. This paper presents an incremental parser of grammar rules for an automatic 3D building reconstruction. The parser enables a model refinement based on new observations with respect to a weighted attribute context-free grammar (WACFG). The falsification or rejection of hypotheses is supported as well. The parser can deal with and adapt available parse trees acquired from previous interpretations or predictions. Parse trees derived so far are updated in an iterative way using transformation rules. A diagnostic step searches for mismatches between current and new nodes. Prior knowledge on fac¸ades is incorporated. It is given by probability densities as well as architectural patterns. Since we cannot always assume normal distributions, the derivation of location and shape parameters of building objects is based on a kernel density estimation (KDE). While the level of detail is continuously improved, the geometrical, semantic and topological consistency is ensured.

scholarly journals AN EMPRICAL POINT ERROR MODEL FOR TLS DERIVED POINT CLOUDS

2016 ◽  
Vol XLI-B5 ◽  
pp. 557-563 ◽  
Author(s):  
Mustafa Ozendi ◽  
Devrim Akca ◽  
Hüseyin Topan
Keyword(s):  
Real World ◽  
Incidence Angle ◽  
A Priori ◽  
Laser Scanner ◽  
Point Clouds ◽  
Error Model ◽  
Random Errors ◽  
Data Set ◽  
3D Point Clouds

The random error pattern of point clouds has significant effect on the quality of final 3D model. The magnitude and distribution of random errors should be modelled numerically. This work aims at developing such an anisotropic point error model, specifically for the terrestrial laser scanner (TLS) acquired 3D point clouds. A priori precisions of basic TLS observations, which are the range, horizontal angle and vertical angle, are determined by predefined and practical measurement configurations, performed at real-world test environments. A priori precision of horizontal (𝜎<sub>𝜃</sub>) and vertical (𝜎<sub>𝛼</sub>) angles are constant for each point of a data set, and can directly be determined through the repetitive scanning of the same environment. In our practical tests, precisions of the horizontal and vertical angles were found as 𝜎<sub>𝜃</sub>=±36.6<sup>𝑐𝑐</sup> and 𝜎<sub>𝛼</sub>=±17.8<sup>𝑐𝑐</sup>, respectively. On the other hand, a priori precision of the range observation (𝜎<sub>𝜌</sub>) is assumed to be a function of range, incidence angle of the incoming laser ray, and reflectivity of object surface. Hence, it is a variable, and computed for each point individually by employing an empirically developed formula varying as 𝜎<sub>𝜌</sub>=±2−12 𝑚𝑚 for a FARO Focus X330 laser scanner. This procedure was followed by the computation of error ellipsoids of each point using the law of variance-covariance propagation. The direction and size of the error ellipsoids were computed by the principal components transformation. The usability and feasibility of the model was investigated in real world scenarios. These investigations validated the suitability and practicality of the proposed method.

scholarly journals Semantic Mapping for Autonomous Subsea Intervention

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6740
Author(s):  
Guillem Vallicrosa ◽  
Khadidja Himri ◽  
Pere Ridao ◽  
Nuno Gracias
Keyword(s):  
Object Recognition ◽  
A Priori ◽  
Laser Scanner ◽  
Point Clouds ◽  
Bayesian Techniques ◽  
Semantic Map ◽  
3D Point Clouds ◽  
Feature Based ◽  
Object Classes ◽  
High Level

This paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based slam and 3D object recognition using a database of a priori known objects. The robot uses dvl, pressure, and ahrs sensors for navigation and is equipped with a laser scanner providing non-coloured 3D point clouds of the inspected structure in real time. The object recognition module recognises the pipes and objects within the scan and passes them to the slam, which adds them to the map if not yet observed. Otherwise, it uses them to correct the map and the robot navigation if they were already mapped. The slam provides a consistent map and a drift-less navigation. Moreover, it provides a global identifier for every observed object instance and its pipe connectivity. This information is fed back to the object recognition module, where it is used to estimate the object classes using Bayesian techniques over the set of those object classes which are compatible in terms of pipe connectivity. This allows fusing of all the already available object observations to improve recognition. The outcome of the process is a semantic map made of pipes connected through valves, elbows and tees conforming to the real structure. Knowing the class and the position of objects will enable high-level manipulation commands in the near future.

scholarly journals PREDICTION OF BUILDING FLOORPLANS USING LOGICAL AND STOCHASTIC REASONING BASED ON SPARSE OBSERVATIONS

2016 ◽  
Vol IV-2/W1 ◽  
pp. 265-270 ◽  
Author(s):  
S. Loch-Dehbi ◽  
Y. Dehbi ◽  
G. Gröger ◽  
L. Plümer
Keyword(s):  
A Priori ◽  
Gaussian Mixture Models ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Constraint Propagation ◽  
Linear Constraints ◽  
Functional Dependencies ◽  
3D Point Clouds ◽  
Location Parameters ◽  
Novel Method

This paper introduces a novel method for the automatic derivation of building floorplans and indoor models. Our approach is based on a logical and stochastic reasoning using sparse observations such as building room areas. No further sensor observations like 3D point clouds are needed. Our method benefits from an extensive prior knowledge of functional dependencies and probability density functions of shape and location parameters of rooms depending on their functional use. The determination of posterior beliefs is performed using Bayesian Networks. Stochastic reasoning is complex since the problem is characterized by a mixture of discrete and continuous parameters that are in turn correlated by non-linear constraints. To cope with this kind of complexity, the proposed reasoner combines statistical methods with constraint propagation. It generates a limited number of hypotheses in a model-based top-down approach. It predicts floorplans based on a-priori localised windows. The use of Gaussian mixture models, constraint solvers and stochastic models helps to cope with the a-priori infinite space of the possible floorplan instantiations.

scholarly journals AN EMPRICAL POINT ERROR MODEL FOR TLS DERIVED POINT CLOUDS

2016 ◽  
Vol XLI-B5 ◽  
pp. 557-563
Author(s):  
Mustafa Ozendi ◽  
Devrim Akca ◽  
Hüseyin Topan
Keyword(s):  
Real World ◽  
Incidence Angle ◽  
A Priori ◽  
Laser Scanner ◽  
Point Clouds ◽  
Error Model ◽  
Random Errors ◽  
Data Set ◽  
3D Point Clouds

The random error pattern of point clouds has significant effect on the quality of final 3D model. The magnitude and distribution of random errors should be modelled numerically. This work aims at developing such an anisotropic point error model, specifically for the terrestrial laser scanner (TLS) acquired 3D point clouds. A priori precisions of basic TLS observations, which are the range, horizontal angle and vertical angle, are determined by predefined and practical measurement configurations, performed at real-world test environments. A priori precision of horizontal (𝜎<sub>𝜃</sub>) and vertical (𝜎<sub>𝛼</sub>) angles are constant for each point of a data set, and can directly be determined through the repetitive scanning of the same environment. In our practical tests, precisions of the horizontal and vertical angles were found as 𝜎<sub>𝜃</sub>=±36.6<sup>𝑐𝑐</sup> and 𝜎<sub>𝛼</sub>=±17.8<sup>𝑐𝑐</sup>, respectively. On the other hand, a priori precision of the range observation (𝜎<sub>𝜌</sub>) is assumed to be a function of range, incidence angle of the incoming laser ray, and reflectivity of object surface. Hence, it is a variable, and computed for each point individually by employing an empirically developed formula varying as 𝜎<sub>𝜌</sub>=±2−12 𝑚𝑚 for a FARO Focus X330 laser scanner. This procedure was followed by the computation of error ellipsoids of each point using the law of variance-covariance propagation. The direction and size of the error ellipsoids were computed by the principal components transformation. The usability and feasibility of the model was investigated in real world scenarios. These investigations validated the suitability and practicality of the proposed method.

scholarly journals 3D Object Recognition Based on Point Clouds in Underwater Environment with Global Descriptors: A Survey

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4451
Author(s):  
Himri ◽  
Ridao ◽  
Gracias
Keyword(s):  
Object Recognition ◽  
State Of The Art ◽  
A Priori ◽  
Real Data ◽  
Point Clouds ◽  
Performance Comparison ◽  
Research Centre ◽  
Laser Sensor ◽  
3D Point Clouds ◽  
And Performance

This paper addresses the problem of object recognition from colorless 3D point clouds inunderwater environments. It presents a performance comparison of state-of-the-art global descriptors,which are readily available as open source code. The studied methods are intended to assistAutonomous Underwater Vehicles (AUVs) in performing autonomous interventions in underwaterInspection, Maintenance and Repair (IMR) applications. A set of test objects were chosen as beingrepresentative of IMR applications whose shape is typically known a priori. As such, CAD modelswere used to create virtual views of the objects under realistic conditions of added noise and varyingresolution. Extensive experiments were conducted from both virtual scans and from real data collectedwith an AUV equipped with a fast laser sensor developed in our research centre. The underwatertesting was conducted from a moving platform, which can create deformations in the perceived shapeof the objects. These effects are considerably more difficult to correct than in above-water counterparts,and therefore may affect the performance of the descriptor. Among other conclusions, the testing weconducted illustrated the importance of matching the resolution of the database scans and test scans,as this significantly impacted the performance of all descriptors except one. This paper contributes tothe state-of-the-art as being the first work on the comparison and performance evaluation of methodsfor underwater object recognition. It is also the first effort using comparison of methods for dataacquired with a free floating underwater platform.

scholarly journals PHOTOGRAMMETRIC 3D BUILDING RECONSTRUCTION FROM THERMAL IMAGES

2017 ◽  
Vol IV-2/W3 ◽  
pp. 25-32 ◽  
Author(s):  
E. Maset ◽  
A. Fusiello ◽  
F. Crosilla ◽  
R. Toldo ◽  
D. Zorzetto
Keyword(s):  
Computer Vision ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Geometric Accuracy ◽  
Icp Algorithm ◽  
3D Point Clouds ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
Calibration Parameters ◽  
Building Reconstruction

This paper addresses the problem of 3D building reconstruction from thermal infrared (TIR) images. We show that a commercial Computer Vision software can be used to automatically orient sequences of TIR images taken from an Unmanned Aerial Vehicle (UAV) and to generate 3D point clouds, without requiring any GNSS/INS data about position and attitude of the images nor camera calibration parameters. Moreover, we propose a procedure based on Iterative Closest Point (ICP) algorithm to create a model that combines high resolution and geometric accuracy of RGB images with the thermal information deriving from TIR images. The process can be carried out entirely by the aforesaid software in a simple and efficient way.

scholarly journals Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1228
Author(s):  
Ting On Chan ◽  
Linyuan Xia ◽  
Yimin Chen ◽  
Wei Lang ◽  
Tingting Chen ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Geometric Model ◽  
Digital Camera ◽  
Point Clouds ◽  
Symmetry Analysis ◽  
3D Point Clouds ◽  
Transmission Towers ◽  
Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

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