We present MonteBoxFinder, a method that, given a noisy input point cloud, fits cuboids to the input scene. Our primary contribution is a discrete optimization algorithm that, from a dense set of initially detected cuboids, is able to efficiently filter good boxes from the noisy ones. Inspired by recent applications of MCTS to scene understanding problems, we develop a stochastic algorithm that is, by design, more efficient for our task. Indeed, the quality of a fit for a cuboid arrangement is invariant to the order in which the cuboids are added into the scene. We develop several search baselines for our problem and demonstrate, on the ScanNet dataset, that our approach is more efficient and precise. Finally, we strongly believe that our core algorithm is very general and that it could be extended to many other problems in 3D scene understanding.
翻译:我们向蒙特- 福克斯- 芬德( MonteBoxFinder) 介绍一种方法,它考虑到输入点云的噪音,使幼崽适合输入场景。我们的主要贡献是使用一种离散的优化算法,从一组最初检测到的幼崽中,能够有效地从噪音的幼崽中过滤好箱子。我们受最近MCTS应用于现场了解问题的启发,我们开发一种随机算法,通过设计,更高效地完成我们的任务。事实上,适合幼崽安排的质量与将幼崽添加到现场的顺序是不一致的。我们为我们的问题开发了几个搜索基线,并在扫描网数据集上展示了我们的方法更有效率和精确。最后,我们坚信我们的核心算法非常笼统,可以在3D 场理解中推广到许多其他问题。