The 3D Hough Transform for plane detection in point clouds: A review and a new accumulator design

Borrmann, Dorit; Elseberg, Jan; Lingemann, Kai; Nüchter, Andreas

doi:10.1007/3dres.02(2011)3

Cited by 295 publications

(207 citation statements)

References 11 publications

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“…The extension of this algorithm to 3D spaces allows the detection of planes [19]. The idea is to apply the Hough transform to the points T a = (x a ; y a ; z a ) and T b = (x b ; y b ; z b ) lying on the ellipsoid's surface.…”

Section: Reflector Estimationmentioning

confidence: 99%

“…The 3D Hough transform is then applied to the subset of points obtained, ρ = x cos(θ) sin(φ) + y sin(θ) sin(φ) + z cos(φ), where ρ is the distance from the center of the polar coordinate system, θ is the azimuth and φ the elevation [19]. Using an accumulator, the coefficients of the best fitting plane are given by p 1 = cos(θ max ) sin(φ max ), p 2 = Fig.…”

Section: Reflector Estimationmentioning

confidence: 99%

See 1 more Smart Citation

A 3D model for room boundary estimation

Remaggi

Jackson

Wang

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Estimating the geometric properties of an indoor environment through acoustic room impulse responses (RIRs) is useful in various applications, e.g., source separation, simultaneous localization and mapping, and spatial audio. Previously, we developed an algorithm to estimate the reflector's position by exploiting ellipses as projection of 3D spaces. In this article, we present a model for full 3D reconstruction of environments. More specifically, the three components of the previous method, respectively, MUSIC for direction of arrival (DOA) estimation, numerical search adopted for reflector estimation and the Hough transform to refine the results, are extended for 3D spaces. A variation is also proposed using RANSAC instead of the numerical search and the Hough transform wich significantly reduces the run time. Both methods are tested on simulated and measured RIR data. The proposed methods perform better than the baseline, reducing the estimation error.

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Section: Reflector Estimationmentioning

confidence: 99%

Section: Reflector Estimationmentioning

confidence: 99%

A 3D model for room boundary estimation

Remaggi

Jackson

Wang

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Use of the 3D Hough transform for extraction of planar faces from point clouds was investigated by Vosselman and Dijkman. Randomized Hough Transform is a variant of the 3D Hough transform that has proven to be especially effective for plane detection in point clouds [27,28].…”

Section: Hough Transformmentioning

confidence: 99%

Automated Removal of Planar Clutter from 3D Point Clouds for Improving Industrial Object Recognition

Czerniawski

Nahangi

Walbridge

et al. 2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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The industrial construction industry makes use of prefabrication, preassembly, modularization and offsite fabrication (PPMOF) for project execution because they offer a superior level of control as compared to on-site operations. This control is enabled by systematic and thorough performance feedback loops. Improvement of the feedback systems within these facilities will require a transition away from suboptimal manual data collection to more reliable automated data collection and processing. Laser scanners are an effective tool for automatically gathering dimensional data but extraction of useful information from point clouds remains a challenge. The speed of 3D object recognition methods depends on the size of the search space. Methods for reducing this search space are needed in order to improve the performance of 3D object recognition and subsequent information extraction. Large planar objects (e.g. floors and walls) constitute a large portion of the search space in fabrication facilities, yet are rarely the objects of interest for analysis. In this paper, an automated framework for detecting and removing large planes in point clouds is presented to speed up object recognition. The raw point cloud is first Guassian mapped to normal vector space by calculating normal vectors at each point. The Gaussian sphere is clustered using a density-based clustering algorithm and major parallel planes are segmented from the rest of the point cloud. The major planes are removed and the remaining objects in the scene continue on to 3D object recognition. Results show the algorithm for automatic plane removal can reduce the search space for object recognition by as much as 60% or 70%.

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“…Various research has been done on the automatic processing of point clouds collected by airborne scanners [1,2]. There are also many scientific studies on the detection of primitives or specifically-shaped objects in point clouds [3,4]. Some of them can be implemented in processing MLS point clouds.…”

Section: Introductionmentioning

confidence: 99%

Catenary System Detection, Localization and Classification Using Mobile Scanning Data

Pastucha

2016

Remote Sensing

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This paper presents a new method for detecting, locating and classifying overhead contact systems (catenary systems) in point clouds collected by mobile mapping systems (MMS) on rail roads. Contrary to many other application types, railway embankments are highly regulated and standardized. Railway infrastructure geometric relations remain roughly unchanged within established regions and have similarities between them. The newly-developed method exploits both these characteristics, as well as the survey process. There are several steps in this approach. Firstly, it restricts the search for catenaries relative to the distance to registered MMS trajectory, then finds possible support structures according to the density of points above the track. Subsequently, the method verifies the structures' presence and classifies the points with the use of the RANSAC algorithm. It establishes the presence of cantilevers, as well as poles or structural beams, depending on the type of detected support structure. The method also determines the coordinates of the identified object on the ground. Finally, a classification is clarified with the use of a modified DBSCAN algorithm. The design method has been verified with data collected in four surveys where the cumulative length of the route was almost 90 km. Over 97% of support structures were correctly detected, and out of these, over 95% were completely classified.

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The 3D Hough Transform for plane detection in point clouds: A review and a new accumulator design

Cited by 295 publications

References 11 publications

A 3D model for room boundary estimation

A 3D model for room boundary estimation

Automated Removal of Planar Clutter from 3D Point Clouds for Improving Industrial Object Recognition

Catenary System Detection, Localization and Classification Using Mobile Scanning Data

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