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PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds

2020-12-02 06:20:54
Yi Wei, Ziyi Wang, Yongming Rao, Jiwen Lu, Jie Zhou

Abstract

tract: 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.

Abstract (translated)

URL

https://arxiv.org/abs/2012.00987

PDF

https://arxiv.org/pdf/2012.00987


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