Detecting Objects in Scene Point Cloud: A Combinational Approach

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 multi-object detection in continuous point clouds. The framework not only takes global and local information into account, but also benefits from both learning and empirical methods. The experiments performed on the challenging ground Lidar dataset show the effectiveness of our method.