论文标题
MonteboxFinder：检测和过滤原始词以适合嘈杂的点云
MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point Cloud
论文作者
论文摘要
我们提出了MonteboxFinder，该方法给定嘈杂的输入点云将立方体适合输入场景。我们的主要贡献是一种离散的优化算法，从一组最初检测到的立方体的密集组中，它能够从嘈杂的盒子中有效地过滤好盒子。受到MCT在了解现场问题的最新应用的启发，我们开发了一种随机算法，该算法通过设计更有效地为我们的任务而言。实际上，适合立方体布置的质量对于将立方体添加到场景的顺序上是不变的。我们为我们的问题开发了几个搜索基准，并在扫描仪数据集上证明了我们的方法更有效，更精确。最后，我们坚信我们的核心算法非常笼统，并且可以将其扩展到3D场景理解中的许多其他问题。
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.
