Abstract
Next Best View computation (NBV) is a long-standing problem in robotics, and consists in identifying the next most informative sensor position(s) for reconstructing a 3D object or scene efficiently and accurately. Like most current methods, we consider NBV prediction from a depth sensor. Learning-based methods relying on a volumetric representation of the scene are suitable for path planning, but do not scale well with the size of the scene and have lower accuracy than methods using a surface-based representation. However, the latter constrain the camera to a small number of poses. To obtain the advantages of both representations, we show that we can maximize surface metrics by Monte Carlo integration over a volumetric representation. Our method scales to large scenes and handles free camera motion: It takes as input an arbitrarily large point cloud gathered by a depth sensor like Lidar systems as well as camera poses to predict NBV. We demonstrate our approach on a novel dataset made of large and complex 3D scenes.
Abstract (translated)
URL
https://arxiv.org/abs/2208.10449
