Abstract
In this paper, we propose Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) to estimate scene flow from point clouds. All-pairs correlations play important roles in scene flow estimation task. However, since point clouds are irregular and unordered, it is challenging to efficiently extract features from all-pairs fields in 3D space. To tackle this problem, we present point-voxel correlation fields, which captures both local and long-range dependencies of point pairs. To capture point-based correlations, we adopt K-Nearest Neighbors search that preserves fine-grained information in the local region. By voxelizing point clouds in a multi-scale manner, a pyramid correlation voxels are constructed to model long-range correspondences. Integrating 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 both synthetic dataset FlyingThings3D and real scenes dataset KITTI. Experimental results show that PV-RAFT surpasses state-of-the-art methods by remarkable margins.
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URL
https://arxiv.org/abs/2012.00987
