Point cloud registration using a viewpoint dictionary

The use of 3D point clouds is currently of much interest. One of the cornerstones of 3D point cloud research and applications is point cloud registration. Given two point clouds, the goal of registration is aligning them in a common coordinate system. In particular, we seek in this work to align a sparse and noisy local point cloud, created from a single stereo pair of images, to a dense and large-scale global point cloud, representing an urban outdoors environment. The common approach of keypoint-based registration, tends to fail due to the sparsity and low quality of the stereo local cloud. We propose here a new approach. It consists of the creation of a dictionary of much smaller clouds using a grid of synthetic viewpoints over the dense global cloud. We then perform registration via an efficient dictionary search. Our approach shows promising results on data acquired in an urban environment.