Abstract-Recognition of three dimensional (3D) objects is a challenging problem, especially in cluttered or occluded scenes. Many existing methods focus on a specific type of object or scene, or require prior segmentation. We describe a robust and efficient general purpose 3D object recognition method that combines machine learning procedures with 3D local features, without a requirement for a priori object segmentation. Experiments validate our method on various object types from engineering and street data scans.
Instead of adopting a model fitting approach, Pang and Neumann REF combined machine learning methods with 3D local features and performed object recognition on cluttered point cloud scenes.
Training-Based Object Recognition in Cluttered 3D Point Clouds
2013 International Conference on 3D Vision
2013 International Conference on 3D Vision
Computer Science