Min-cut based segmentation of point clouds
By Aleksey Golovinskiy, Thomas A. Funkhouser. 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009
307 citations recorded by the source corpus.
Subfields: Computational Mechanics, Environmental Engineering, Geology
View Paper: https://www.semanticscholar.org/paper/4d6e309c71e98471374ba1fd5442318d0be4f826
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Sanja Fidler
- Raquel Urtasun
- Liang-Chieh Chen
- Olga Sorkine-Hornung
- Hoai Bac Le
- Daniela Rus
- Renato Pajarola
- Anh Viet Nguyen
In Environmental Engineering
- Raquel Urtasun
- Sanja Fidler
- Olga Sorkine-Hornung
- Hoai Bac Le
- Daniela Rus
- Renato Pajarola
- Anh Viet Nguyen
- Marcelo H. Ang Jr.
In Geology
- Hoai Bac Le
- Daniela Rus
- Anh Viet Nguyen
- Marcelo H. Ang Jr.
- Raquel Urtasun
- Daniele Panozzo
- Sanja Fidler
- Xinxin Du
Citations in the corpus
- 3D point cloud segmentation: A survey (2013, methodology): “Golovinskiy [38] used k-nearest neighbours (KNN) to build a 3D graph on the point cloud.”
- Object detection and classification from large‐scale cluttered indoor scans (2014, methodology): “Object recognition using learning algorithms has been applied to exterior [GF09, GKF09] and indoor environments [BN10, HHF10, TDS10, KAJS11, SF11, KMYG12, LBRF12, NXS12, SXZ ∗ 12].”
- On the segmentation of 3D LIDAR point clouds (2011, background): “The associated high data rate requires quality conditioning, which can be obtained from segmentation techniques.”
- Graph-based approach for airborne light detection and ranging segmentation (2014, methodology): “Some examples of data-driven image segmentation techniques successfully adapted to 3-D point cloud processing are clustering,(1) region growing,(2,3) or graphbased segmentation.(4,5) When the geometric characteristics of the objects under interest are known, model-based segmentation techniques are o”
- Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “In [59,60], the authors first created a k-nearest neighbors graph, assigned each node according to a background penalty function, added hard foreground constraints, and solved the foreground and background segmentation through min-cut.”
- Beat the MTurkers: Automatic Image Labeling from Weak 3D Supervision (2014, background): “[12] extended this idea to 3D by employing point clouds.”