• Corpus ID: 8549650

EFFICIENT HOUGH TRANSFORM FOR AUTOMATIC DETECTION OF CYLINDERS IN POINT CLOUDS

@inproceedings{Rabbani2005EFFICIENTHT,
  title={EFFICIENT HOUGH TRANSFORM FOR AUTOMATIC DETECTION OF CYLINDERS IN POINT CLOUDS},
  author={Tahir Rabbani and Frank van den Heuvel},
  year={2005}
}
We present an efficient Hough transform for automatic detection of cylinders in point clouds. As cylinders are one of the most frequently used primitives for industrial design, automatic and robust methods for their detection and fitting are essential for reverse engineering from point clouds. The current methods employ automatic segmentation followed by geometric fitting, which requires a lot of manual interaction during modelling. Although Hough transform can be used for automatic detection… 

Figures from this paper

Estimation of cylinder orientation in three-dimensional point cloud using angular distance-based optimization

This work focuses on cylinder parameter estimation in three-dimensional point clouds, proposing a mathematical formulation based on angular distance to determine the cylinder orientation and demonstrating the accuracy and robustness of the technique on synthetically generated cylinder point clouds as well as on real LIDAR data of piping systems.

Automatic recognition of cylinders and planes from unstructured point clouds

The results have shown that the proposed method outperforms alternative approaches in terms of appropriately recognized planes and cylinders without surface type confusion, as well as when the recognition of non-existent primitives is considered.

3D Hough transform for sphere recognition on point clouds

This paper systematically analyze different probabilistic/randomized Hough transform algorithms for spherical object detection in dense point clouds and proposes a new method that combines the advantages of both single-point and multi-point approaches for a faster and more accurate detection.

D Hough Transform for Sphere Recognition on Point Clouds A Systematic Study and a New Method Proposal

This paper systematically analyse different probabilistic/randomized Hough Transform algorithms for spherical object detection in dense point clouds and proposes a new method that combines the advantages of both single-point and multi-point approaches for a faster and more accurate detection.

Extraction of cylinders and estimation of their parameters from point clouds

Automatic Detection of Cylindrical Objects in Built Facilities

AbstractThree-dimensional (3D) facility models are in increasing demand for design, maintenance, operations, and construction project management. For industrial and research facilities, a key focus

Hough-Transform and Extended RANSAC Algorithms for Automatic Detection of 3D Building Roof Planes from Lidar Data

The proposed extension of RANSAC algorithm allows harmonizing the mathematical aspect of the algorithm with the geometry of a roof, and it is shown that the extended approach provides very satisfying results, even in the case of very weak point density and for different levels of building complexity.

Fast Cylinder Shape Matching Using Random Sample Consensus in Large Scale Point Cloud

In this paper, an algorithm is proposed that can perform cylinder type matching faster than the existing method in point clouds that represent space. The existing matching method uses Hough transform
...

References

SHOWING 1-10 OF 31 REFERENCES

3D BUILDING MODEL RECONSTRUCTION FROM POINT CLOUDS AND GROUND PLANS

Airborne laser altimetry has become a very popular technique for the acquisition of digital elevation models. The high point density that can be achieved with this technique enables applications of

Robust Segmentation of Primitives from Range Data in the Presence of Geometric Degeneracy

This paper addresses a common problem in the segmentation of range images. We present methods for the least-squares fitting of spheres, cylinders, cones, and tori to 3D point data, and their

Extracting Cylinders in Full 3D Data Using a Random Sampling Method and the Gaussian Image

A new method for extracting cylinders from an unorganized set of 3D points by separating the ex-traction problem into two distinct steps and using a random sampling method in both steps.

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.

Detection and characterisation of planar fractures using a 3D Hough transform

An Experimental Comparison of Range Image Segmentation Algorithms

A methodology for evaluating range image segmentation algorithms and four research groups have contributed to evaluate their own algorithm for segmenting a range image into planar patches.

Contribution to the Determination of Vanishing Points Using Hough Transform

A method to locate three vanishing points on an image, corresponding to three orthogonal directions of the scene, based on two cascaded Hough transforms is proposed, which is efficient, even in the case of real complex scenes.

Progress in Automated Evaluation of Curved Surface Range Image Segmentation

An automated framework for performance evaluation of curved-surface range image segmentation algorithms is developed and the automated parameter tuning technique is evaluated and found that it compares favorably with manual parameter tuning.

A RANSAC-Based Approach to Model Fitting and Its Application to Finding Cylinders in Range Data

The technique is specifically designed to filter out gross errors before applying a smoothing procedure to compute a precise model in order to solve the problem of locating cylinders in range data.

Surface reconstruction from unorganized points

A general method for automatic reconstruction of accurate, concise, piecewise smooth surfaces from unorganized 3D points that is able to automatically infer the topological type of the surface, its geometry, and the presence and location of features such as boundaries, creases, and corners.