The Devil is in the Pose: Ambiguity-free 3D Rotation-invariant Learning via Pose-aware Convolution
Author:
Affiliation:
1. Shenyang Institute of Automation, Chinese Academy of Sciences,State Key Laboratory of Robotics
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9878378/9878366/09878531.pdf?arnumber=9878531
Reference47 articles.
1. PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds
2. SGMNet: Learning Rotation-Invariant Point Cloud Representations via Sorted Gram Matrix
3. Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
4. Tensor field networks: Rotation-and translation-equivariant neural net-works for 3d point clouds;thomas;ArXiv Preprint,2018
5. Kpconv: Flexible and deformable convolution for point clouds;hugues;Proceedings of the IEEE/CVF International Conference on Computer Vision,2019
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