MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point Cloud

摘要

We present MonteBoxFinder, a method that, given an noisy input point cloud, detects a dense set of imperfect boxes, and employs a discrete optimization algorithm that efficiently explores the space of allbox arrangements in order to find the arrangement that best fits the pointcloud. Our method demonstrates significant superiority of our method over our discrete optimization baselines on the ScanNet dataset, both inefficiency and precision. This is achieved by leveraging the structure of the problem, which is that the fit quality of a cuboid arrangement is invariant to cuboid permutation. Finally, our solution search algorithm is general, as it can be extended to other challenging discrete optimization scenarii.