Automated reconstruction of 3D models from real environments

Vitor, Sequeira; Ng, Kia; Wolfart, Erik; Gonçalves, José; Hogg, David C.

doi:10.1016/s0924-2716(98)00026-4

Cited by 136 publications

(81 citation statements)

References 20 publications

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“…Next we show the application of the constraints to a Bavarian farmhouse reconstructed by the European Commission Joint Research Centre (JRC) [8,15]. The model is shown in figure 5).…”

Section: Resultsmentioning

confidence: 99%

Improving architectural 3D reconstruction by plane and edge constraining

Cantzler

Fisher

Devy

2002

Procedings of the British Machine Vision Conference 2002

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:This paper presents new techniques for improving the structural quality of automatically acquired architectural 3D models. Common architectural features like parallelism and orthogonality of walls and edges are exploited. The location of these features is extracted from the model by using a probabilistic technique (RANSAC). The relationships among the planes and edges are inferred automatically using a knowledge-based architectural model. A numerical algorithm is used to optimise the orientations of the features. Small irregularities in the model are removed by projecting the triangulation vertices onto the features. Planes and edges in the resulting model are aligned to each other. The techniques produce models with improved appearance. We show results for synthetic and real data with consideration of noise. The authors and the University of Edinburgh retain the right to reproduce and publish this paper for non-commercial purposes.Permission is granted for this report to be reproduced by others for non-commercial purposes as long as this copyright notice is reprinted in full in any reproduction. Applications to make other use of the material should be addressed in the first instance to Copyright Permissions, Division of Informatics, The University of Edinburgh, 80 South Bridge, Edinburgh EH1 1HN, Scotland. This paper presents new techniques for improving the structural quality of automatically acquired architectural 3D models. Common architectural features like parallelism and orthogonality of walls and edges are exploited. The location of these features is extracted from the model by using a probabilistic technique (RANSAC). The relationships among the planes and edges are inferred automatically using a knowledge-based architectural model. A numerical algorithm is used to optimise the orientations of the features. Small irregularities in the model are removed by projecting the triangulation vertices onto the features. Planes and edges in the resulting model are aligned to each other. The techniques produce models with improved appearance. We show results for synthetic and real data with consideration of noise. Improving architectural 3D reconstruction by plane and edge constraining

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“…Next we show the application of the constraints to a Bavarian farmhouse reconstructed by the European Commission Joint Research Centre (JRC) [8,15]. The model is shown in figure 5).…”

Section: Resultsmentioning

confidence: 99%

Improving architectural 3D reconstruction by plane and edge constraining

Cantzler

Fisher

Devy

2002

Procedings of the British Machine Vision Conference 2002

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“…The only requirement is that images must be acquired by the same camera with a fixed focal length. After image acquisition, feature points that correspond to high curvature points (corners), are extracted in each image using the Harris corner detector (Sequeira et al, 1999). The maximum number of corners to be extracted in each image is fixed a priori.…”

Section: Building the 3d Model Of Particular Areasmentioning

confidence: 99%

Environment Modelling with an Autonomous Mobile Robot for Cultural Heritage Preservation and Remote Access

Cicirelli¹,

Milella²

2008

Robotics Automation and Control

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IntroductionAs awareness of cultural heritage raised, much effort was devoted, in the last decade, to improve accessibility and preservation of cultural assets. At present, several methods are available that generally make use of laser scanners and cameras to construct 3D photorealistic models of different-sized items, ranging from small objects, like statues, up to large buildings and archaeological sites. These methods provide effective technological solutions for cultural heritage preservation, while guaranteeing, at the same time, their accessibility to as much people as possible. Nevertheless, the generation of models may turn into a timeconsuming procedure if data acquisition and processing is done by hand, since, in order for models to be sufficiently accurate, an extremely painstaking work is required. Contributions to implement automated model building techniques and remote access systems for cultural heritage applications may derive from experience in the mobile robotics field. Recently, several research projects have attempted to develop mobile robotic agents in museums (Burgard et al., 1999;Thrun et al., 2000; Trahanias et al., 2005), with different tasks, such as to supply remote access to distant users, accommodate and guide people in the museum, survey those areas where access is not permitted. Equipped with sensors, like cameras and laser rangefinders, mobile platforms provide a variety of viewpoints and may supply the user with dedicated tours of the exhibition and personalized tele-presence, which result in greater interaction capabilities than fixed or even remotely controllable cameras (Trahanias et al., 2005). Generally, in order for a mobile robot to perform its tasks, the knowledge of a map of the environment is needed. Hence, a number of methods for efficient environment modeling, based on information from onboard robot sensors, have been also developed. Yet, relatively little work has been done to extend these techniques to cultural heritage applications, such as modeling of historical and archaeological sites. In this chapter, we describe our research concerning the development of methods for environment exploration and modelling by a multisensor mobile platform, in the context of cultural heritage access and preservation. Our goal is to have a system able to navigate in the environment and acquire sensorial data, in order to either construct global or local models of the site, or send information to a remote console.

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“…The ICP algorithm can also be computationally intensive and time-consuming in its search for conjugate points in overlapping scans (Kang et. al., 2009;Sequeira, 1999). At present, research works aiming at elimination of measuring marks from the orientation process are being carried out (Kang et.…”

Section: Introductionmentioning

confidence: 99%

“…At present, research works aiming at elimination of measuring marks from the orientation process are being carried out (Kang et. al., 2009;Sequeira, 1999). Recent works are focused on orientation of TLS point clouds for estimation of transformation parameters by means of recognition of shapes on scans .The existing approaches are based on detection of corresponding characteristic features in a point cloud, such as lines or planes.…”

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confidence: 99%

The Use of Computer Vision Algorithms for Automatic Orientation of Terrestrial Laser Scanning Data

Markiewicz¹

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission III, WG III/2 KEY WORDS: Feature Extraction, Scan Registration, Computer Vision, analysis of image matching algorithms, TLS ABSTRACT:The paper presents analysis of the orientation of terrestrial laser scanning (TLS) data. In the proposed data processing methodology, point clouds are considered as panoramic images enriched by the depth map. Computer vision (CV) algorithms are used for orientation, which are applied for testing the correctness of the detection of tie points and time of computations, and for assessing difficulties in their implementation. The BRISK, FASRT, MSER, SIFT, SURF, ASIFT and CenSurE algorithms are used to search for key-points. The source data are point clouds acquired using a Z+F 5006h terrestrial laser scanner on the ruins of Iłża Castle, Poland. Algorithms allowing combination of the photogrammetric and CV approaches are also presented. INTRODUCTIONPoint clouds are acquired from terrestrial laser scanning (TLS) in a local reference system of the instrument. For a large number of data sets (where big and complex objects are processed),the point clouds need to be transformed for a global reference system. This process consists of determination of orientation parameters, i.e.,three angles of rotation and three elements of linear transformation. For this purpose,affine 3D transformation is performed; it is also possible to achieve 3D transformation by similarity, when a scale difference exists between the exterior and the scanner co-ordinate systems. This transformation is performed based on control points, which should be evenly distributed across the entire area of the whole point clouds . At least three points are required; when their number is increased, redundant observations are generated, which allows gross errors to be eliminated and increases the orientation accuracy. The point-based registration method is the one most commonly applied for TLS data orientation (Vosselman, Mass, 2010). There are two types of this method: target-based and ICP. The target-based method is the conventional approach to pointbased orientation, and is usually performed with the use of measuring marks, which are previously distributed on the object. The iterative method, ICP, involves the detection and comparison of nearest pairs of points in clouds (McKay, 1992). The ICP algorithm requires a good first approximation in order to converge to a global minimum. However, even if there is considerable overlap, convergence to a global minimum is not guaranteed. The ICP algorithm can also be computationally intensive and time-consuming in its search for conjugate points in overlapping scans (Kang et. al., 2009;Sequeira, 1999). At present, research works aiming at elimination of measuring marks from the orientation process are being carried out (Kang et. al., 2009;Sequeira, 1999). Recent works are focused on orientation of TLS point clouds for estimation of transformation parameters by means of recognition of shapes on scans .The existing approaches are based on detection of corresponding characteristic ...

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Automated reconstruction of 3D models from real environments

Cited by 136 publications

References 20 publications

Improving architectural 3D reconstruction by plane and edge constraining

Improving architectural 3D reconstruction by plane and edge constraining

Environment Modelling with an Autonomous Mobile Robot for Cultural Heritage Preservation and Remote Access

The Use of Computer Vision Algorithms for Automatic Orientation of Terrestrial Laser Scanning Data

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