KPPF: Keypoint-Based Point-Pair-Feature for Scalable Automatic Global Registration of Large RGB-D Scans

Lucas Malleus, Thomas Fisichella, Diane Lingrand, Frédéric Precioso, N. Gros, Y. Noutary, L. Robert, Lirone Samoun

Abstract: One of the most important challenges in the field of 3D data processing is to be able to reconstruct a complete 3D scene with a high accuracy from several captures. Usually this process is achieved through two main phases: a coarse, or rough, alignment step then a fine alignment. In this article we propose an automatic scalable global registration method (i.e. without arbitrary pose of the sensor) under the following constraints: markerless, very large scale data (several, potentially many millions of points per scans), little overlap between scans, for more than two or three dozens of scans, without a priori knowledge on the 6 degrees of freedom. Here we only address the coarse alignment, and consider the fine alignment step tackled by dedicated existing approaches such as Iterative Closest Point (ICP) [3]. We evaluate thoroughly our method on our own dataset of 33 real large scale scans of an indoor building. The data presents some pairs of scans with very little overlap, architectural challenges (a patio and a rotunda open through several levels of the buildings, etc), several millions of points per scan. We will make this dataset public as part of a benchmark available for the community. We have thus evaluated the accuracy of our method, the scalability to the initial amount of points and the robustness to occlusions, little scan overlap and architectural challenges.