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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2023.3294355
|2 doi
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|a eng
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| 100 |
1 |
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|a Wang, Ziyi
|e verfasserin
|4 aut
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| 245 |
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|a 3D Point-Voxel Correlation Fields for Scene Flow Estimation
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|c 2023
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|a Text
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|a Date Revised 03.10.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a In this paper, we propose Point-Voxel Correlation Fields to explore relations between two consecutive point clouds and estimate scene flow that represents 3D motions. Most existing works only consider local correlations, which are able to handle small movements but fail when there are large displacements. Therefore, it is essential to introduce all-pair correlation volumes that are free from local neighbor restrictions and cover both short- and long-term dependencies. However, it is challenging to efficiently extract correlation features from all-pairs fields in the 3D space, given the irregular and unordered nature of point clouds. To tackle this problem, we present point-voxel correlation fields, proposing distinct point and voxel branches to inquire about local and long-range correlations from all-pair fields respectively. To exploit point-based correlations, we adopt the K-Nearest Neighbors search that preserves fine-grained information in the local region, which guarantees the scene flow estimation precision. By voxelizing point clouds in a multi-scale manner, we construct pyramid correlation voxels to model long-range correspondences, which are utilized to handle fast-moving objects. Integrating these two types of correlations, we propose Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) architecture that employs an iterative scheme to estimate scene flow from point clouds. To adapt to different flow scope conditions and obtain more fine-grained results, we further propose Deformable PV-RAFT (DPV-RAFT), where the Spatial Deformation deforms the voxelized neighborhood, and the Temporal Deformation controls the iterative update process. We evaluate the proposed method on the FlyingThings3D and KITTI Scene Flow 2015 datasets and experimental results show that we outperform state-of-the-art methods by remarkable margins
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|a Journal Article
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| 700 |
1 |
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|a Wei, Yi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Rao, Yongming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Jie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lu, Jiwen
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 11 vom: 26. Nov., Seite 13621-13635
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|x 1939-3539
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|g volume:45
|g year:2023
|g number:11
|g day:26
|g month:11
|g pages:13621-13635
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|u http://dx.doi.org/10.1109/TPAMI.2023.3294355
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