论文标题
PV-RAFT：用于点云的场景流量估计的点伏氧化相关字段
PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds
论文作者
论文摘要
在本文中，我们提出了一个点素反复的全对场转换（PV-RAFT）方法，以估算从点云的场景流。由于点云是不规则且无序的，因此从3D空间中的全对字段中提取特征是具有挑战性的，在3D空间中，全对相关性在场景流量估计中起着重要作用。为了解决这个问题，我们提出了点素的相关场，该字段同时捕获了点对的局部和远程依赖性。为了捕获基于点的相关性，我们采用了k-near最邻居搜索，该搜索可保留本地区域的细粒度信息。通过以多尺度的方式体素化点云，我们构造了金字塔相关体素，以建模远程对应关系。整合了这两种类型的相关性，我们的PV-RAFT利用了全对关系来处理小型和大型位移。我们评估了2015年Flaythings3D和Kitti场景流的拟议方法。实验结果表明，PV-RAFT优于明显边缘的最先进方法。
In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds. Since point clouds are irregular and unordered, it is challenging to efficiently extract features from all-pairs fields in the 3D space, where all-pairs correlations play important roles in scene flow estimation. To tackle this problem, we present point-voxel correlation fields, which capture both local and long-range dependencies of point pairs. To capture point-based correlations, we adopt the K-Nearest Neighbors search that preserves fine-grained information in the local region. By voxelizing point clouds in a multi-scale manner, we construct pyramid correlation voxels to model long-range correspondences. Integrating these two types of correlations, our PV-RAFT makes use of all-pairs relations to handle both small and large displacements. We evaluate the proposed method on the FlyingThings3D and KITTI Scene Flow 2015 datasets. Experimental results show that PV-RAFT outperforms state-of-the-art methods by remarkable margins.
