The Influence of the Input Parameters Selection on the RANSAC Results

Urbančič, Tilen; Fras, Mojca Kosmatin; Stopar, Bojan

doi:10.2507/ijsimm13(2)3.258

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…It is assumed for the estimation that there are more points on the facade plane than in front of or behind. The MSAC method is used to estimate the parameters (Urbančič et al, 2014). The inliers now make up the facade.…”

Section: Plane Estimationmentioning

confidence: 99%

Automatic Extraction of Facades and Windows From MLS Point Clouds Using Voxelspace and Visibility Analysis

Hoegner

Gleixner

2022

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

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Abstract. This contribution presents a method for extracting a 3D model of facades and windows from a point cloud. The point cloud is segmented based on a voxel octree, in which the facades are sought as planes. These can be used to filter out potential window points within the building, which are then analysed on their visibility by checking the occupancy grid of the voxel space. Here, methods of digital image processing are used for analysing both point clusters behind the facade and holes in the estimated facade planes as window candidates. Facades and windows are both simplified as rectangles. The test data set was gathered in a Mobile Laser Scanning campaign. While the segmentation fails in some cases, the extraction of facades and windows shows good results: 25 facades with 702 detected windows yield a detection rate of 86% with a false alarm rate of 13%. The reconstructed sizes of the windows differ from reference measurements in the range of centimetres to a few decimetres. These refined geometries can be used to enrich existing building models or for vehicle navigation without GNSS.

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Section: Plane Estimationmentioning

confidence: 99%

Automatic Extraction of Facades and Windows From MLS Point Clouds Using Voxelspace and Visibility Analysis

Hoegner

Gleixner

2022

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

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“…The optimal choice for these variables can depend greatly on the specifics of the problem. Theoretical results exist that bound the maximum number of iterations with respect to the percentage of inliers present in the data [16].…”

Section: B Limitationsmentioning

confidence: 99%

Geolocation with FDOA measurements via polynomial systems and RANSAC

Cameron¹,

Bates²

2018

2018 IEEE Radar Conference (RadarConf18)

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The problem of geolocation of a transmitter via time difference of arrival (TDOA) and frequency difference of arrival (FDOA) is given as a system of polynomial equations. This allows for the use of homotopy continuation-based methods from numerical algebraic geometry. A novel geolocation algorithm employs numerical algebraic geometry techniques in conjunction with the random sample consensus (RANSAC) method. This is all developed and demonstrated in the setting of only FDOA measurements, without loss of generality. Additionally, the problem formulation as polynomial systems immediately provides lower bounds on the number of receivers or measurements required for the solution set to consist of only isolated points.

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“…Iz obeh lahko enostavno razberemo koordinate osmih vogalnih točk okna (slika 11). Drugi način vključuje uporabo algoritma Ransac (Urbančič et al, 2014). Iz skeniranega oblaka točk postopoma poiščemo šest največjih ravnin s toleranco 0,5 cm.…”

Section: Kakovost Iskanja Robov Oken Iz Oblaka Točk Skeniranega Detajla Glede Na Izmero Tpsunclassified