Multisensor Fusion for Volumetric Reconstruction of Large Outdoor Areas

Fiocco, Marco; Boström, G.; Gonçalves, José; Vitor, Sequeira

doi:10.1109/3dim.2005.60

Cited by 21 publications

(31 citation statements)

References 14 publications

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“…9,10 Fiocco et al 20 had previously defined a reflectivity quality metric as a binary quality metric; however, their approach reduced the generalizability of the metric.…”

Section: Additional Quality Metricsmentioning

confidence: 99%

“…The beam diameter orthogonal to the beam axis is smallest at the beam waist, so this represents the ideal distance to any surface in the total field of view. Fiocco et al 20 also defined a distance quality metric based on the minimum and maximum range limits, while Sequeira et al…”

Section: Additional Quality Metricsmentioning

confidence: 99%

“…9,10 Surface orientation is a commonly used quality metric; [18][19][20][21][22][23] however, previous versions of this metric did not employ a maximum acceptable surface orientation as part of the metric. The metric c i orient is a neighborhood-based metric because it is based on an estimate of the surface normal, so its quality can be assessed using c i enc .…”

Section: Additional Quality Metricsmentioning

confidence: 99%

See 2 more Smart Citations

Measurement quality metrics for rapid laser range scanning

MacKinnon

2010

J. Electron. Imaging

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“…9,10 Fiocco et al 20 had previously defined a reflectivity quality metric as a binary quality metric; however, their approach reduced the generalizability of the metric.…”

Section: Additional Quality Metricsmentioning

confidence: 99%

Section: Additional Quality Metricsmentioning

confidence: 99%

Section: Additional Quality Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Measurement quality metrics for rapid laser range scanning

MacKinnon

2010

J. Electron. Imaging

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“…Polyhedral 3D models are commonly constructed on the basis of heuristics for extracting and connecting 3D lines along the segment boundaries (Dorninger and Pfeifer, 2008;Vosselman and Dijkman, 2001;Sohn et al, 2008;Rau and Lin, 2011;Rottensteiner, 2003). Structural modeling procedures estimate the coordinates of the building model's 3D vertices by error propagation techniques (Lafarge and Mallet, 2012) or lo-cal error minimization (Fiocco et al, 2005) are less frequently used. The latter technique is also used by Zhou and Neumann (2010), who apply a 2.5D dual contouring algorithm on a point cloud which is segmented into different height layers.…”

Section: Related Workmentioning

confidence: 99%

3d Building Reconstruction From Lidar Point Clouds by Adaptive Dual Contouring

Orthuber

Avbelj

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents a novel workflow for data-driven building reconstruction from Light Detection and Ranging (LiDAR) point clouds. The method comprises building extraction, a detailed roof segmentation using region growing with adaptive thresholds, segment boundary creation, and a structural 3D building reconstruction approach using adaptive 2.5D Dual Contouring. First, a 2D-grid is overlain on the segmented point cloud. Second, in each grid cell 3D vertices of the building model are estimated from the corresponding LiDAR points. Then, the number of 3D vertices is reduced in a quad-tree collapsing procedure, and the remaining vertices are connected according to their adjacency in the grid. Roof segments are represented by a Triangular Irregular Network (TIN) and are connected to each other by common vertices or -at height discrepancies -by vertical walls. Resulting 3D building models show a very high accuracy and level of detail, including roof superstructures such as dormers. The workflow is tested and evaluated for two data sets, using the evaluation method and test data of the "ISPRS Test Project on Urban Classification and 3D Building Reconstruction" (Rottensteiner et al., 2012). Results show that the proposed method is comparable with the state of the art approaches, and outperforms them regarding undersegmentation and completeness of the scene reconstruction.

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“…Many algorithms have been studied during the last years for automatic 3D reconstruction [1,2] from image analysis for application in different fields. In [3] a semi-automatic system is described that is based on a 3D reconstruction of a museum environment, obtained by a stereo vision sensor: proposed system is able to detect interesting events and to guide the users into the museum.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Scene Reconstruction for 3D Virtual Guidance

Calbi

Regazzoni

2006

Lecture Notes in Computer Science

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Abstract. In this paper a system is presented able to reproduce the actions of multiple moving objects into a 3D model. A multi-camera system is used for automatically detect, track and classify the objects. Data fusion from multiple sensors allows to get a more precise estimation of the position of detected moving objects and to solve occlusions problem. These data are then used to automatically place and animate objects avatars in a 3D virtual model of the scene, thus allowing users connected to this system to receive a 3D guide into the monitored environment.

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Multisensor Fusion for Volumetric Reconstruction of Large Outdoor Areas

Cited by 21 publications

References 14 publications

Measurement quality metrics for rapid laser range scanning

Measurement quality metrics for rapid laser range scanning

3d Building Reconstruction From Lidar Point Clouds by Adaptive Dual Contouring

Dynamic Scene Reconstruction for 3D Virtual Guidance

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