Fast and Robust Multi-view 3D Object Recognition in Point Clouds

Recognition of three dimensional (3D) objects in point clouds is a challenging problem. Existing methods often require prior segmentation or 3D descriptor training and matching, both time consuming and complex processes, especially for large-scale industrial or urban street data. We describe a new recognition approach that projects a 3D point cloud into several 2D depth images from multiple viewpoints, transforming the 3D recognition problem into a series of 2D detection problems. This method reduces complexity, stabilizes performance, and significantly speeds up the recognition process, without any requirement for object segmentation or detector training. Experiments validate the superiority of our method over several state-of-the-art methods on examples from industrial and street data scans.