论文标题
运行：在最佳运输中引导的点云上的场景流动
FLOT: Scene Flow on Point Clouds Guided by Optimal Transport
论文作者
论文摘要
我们提出并研究了一种称为FLOT的方法，该方法估计了点云上的场景流。我们通过注意到点云上的场景流量估算减少到估计一个完美世界中的排列矩阵来启动FLOT的设计。受图形匹配的最新作品的启发，我们通过从最佳传输中借用工具来构建一种方法来查找这些对应关系。然后，我们放松运输约束，以考虑现实世界的瑕疵。两点之间的运输成本是由使用合成数据集在全面监督下训练的神经网络提取的深度特征之间的成对相似性。我们的主要发现是，FLOT可以在合成和现实世界数据集上执行最佳现有方法，同时需要更少的参数，而无需使用多尺度分析。我们的第二个发现是，在考虑的培训数据集中，大多数性能都可以通过学习的运输成本来解释。这产生了一种更简单的方法，即Flot $ _0 $，它使用特定的最佳传输参数选择获得，并且执行几乎和flot一样。
We propose and study a method called FLOT that estimates scene flow on point clouds. We start the design of FLOT by noticing that scene flow estimation on point clouds reduces to estimating a permutation matrix in a perfect world. Inspired by recent works on graph matching, we build a method to find these correspondences by borrowing tools from optimal transport. Then, we relax the transport constraints to take into account real-world imperfections. The transport cost between two points is given by the pairwise similarity between deep features extracted by a neural network trained under full supervision using synthetic datasets. Our main finding is that FLOT can perform as well as the best existing methods on synthetic and real-world datasets while requiring much less parameters and without using multiscale analysis. Our second finding is that, on the training datasets considered, most of the performance can be explained by the learned transport cost. This yields a simpler method, FLOT$_0$, which is obtained using a particular choice of optimal transport parameters and performs nearly as well as FLOT.
