Residual 3D Scene Flow Learning with Context-Aware Feature Extraction
- URL: http://arxiv.org/abs/2109.04685v1
- Date: Fri, 10 Sep 2021 06:15:18 GMT
- Title: Residual 3D Scene Flow Learning with Context-Aware Feature Extraction
- Authors: Guangming Wang, Yunzhe Hu, Xinrui Wu, Hesheng Wang
- Abstract summary: We propose a novel context-aware set conv layer to exploit contextual structure information of Euclidean space.
We also propose an explicit residual flow learning structure in the residual flow refinement layer to cope with long-distance movement.
Our method achieves state-of-the-art performance, surpassing all other previous works to the best of our knowledge by at least 25%.
- Score: 11.394559627312743
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Scene flow estimation is the task to predict the point-wise 3D displacement
vector between two consecutive frames of point clouds, which has important
application in fields such as service robots and autonomous driving. Although
many previous works have explored greatly on scene flow estimation based on
point clouds, we point out two problems that have not been noticed or well
solved before: 1) Points of adjacent frames in repetitive patterns may be
wrongly associated due to similar spatial structure in their neighbourhoods; 2)
Scene flow between adjacent frames of point clouds with long-distance movement
may be inaccurately estimated. To solve the first problem, we propose a novel
context-aware set conv layer to exploit contextual structure information of
Euclidean space and learn soft aggregation weights for local point features.
Our design is inspired by human perception of contextual structure information
during scene understanding. We incorporate the context-aware set conv layer in
a context-aware point feature pyramid module of 3D point clouds for scene flow
estimation. For the second problem, we propose an explicit residual flow
learning structure in the residual flow refinement layer to cope with
long-distance movement. The experiments and ablation study on FlyingThings3D
and KITTI scene flow datasets demonstrate the effectiveness of each proposed
component and that we solve problem of ambiguous inter-frame association and
long-distance movement estimation. Quantitative results on both FlyingThings3D
and KITTI scene flow datasets show that our method achieves state-of-the-art
performance, surpassing all other previous works to the best of our knowledge
by at least 25%.
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