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Object detection is a fundamental task in computer vision. As the 3D scanning techniques become popular, directly detecting objects through 3D point cloud of a scene becomes an immediate need. We propose an object detection framework combining learning-based classification, local descriptor, a new variance of RANSAC imposing rigid-body constraint and an iterative process for multiobject detection in continuous point clouds. The framework not only takes global and local information into account,doi:10.1109/3dv.2013.31 dblp:conf/3dim/HuangY13 fatcat:txhfr2zbjbflfeqskx34ekjbwa