EFFICIENT HOUGH TRANSFORM FOR AUTOMATIC DETECTION OF CYLINDERS IN POINT CLOUDS

By Tahir Rabbani, Frank van den Heuvel. 2005

311 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3000928b364eace85f4cbd59b83d1706b1456a51

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1. Scott Drew Pendleton
2. Slobodan Ilic
3. Xinxin Du
4. Marcelo H. Ang Jr.
5. Hans Andersen
6. Bertram Drost
7. Nassir Navab
8. Daniela Rus

Citations in the corpus

• Model globally, match locally: Efficient and robust 3D object recognition (2010, methodology): “Several approaches detect objects using a variant of the Generalized Hough Transform [8] [14] [25] but are limited to primitive objects as the recovery of a full 3D pose with 6 degrees of freedom is computationally too expensive.”
• Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “In [53,54], the 3D HT was applied on point level and normal vectors to identify planar structures in the point clouds, whereas in [55], the authors proposed a sequential HT algorithm to detect cylinders in the point cloud.”
• Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion (2009, background): “[ 3 ], [4]. This, however, is computationally demanding and hardly applicable in real-time.”