Rotation Transformation Network: Learning View-Invariant Point Cloud for Classification and Segmentation
Not on the map. Why?
Abstract (arXiv)
Many recent works show that a spatial manipulation module could boost the performances of deep neural networks (DNNs) for 3D point cloud analysis. In this paper, we aim to provide an insight into spatial manipulation modules. Firstly, we find that the smaller the rotational degree of freedom (RDF) of objects is, the more easily these objects are handled by these DNNs. Then, we investigate the effect of the popular T-Net module and find that it could not reduce the RDF of objects. Motivated by the above two issues, we propose a rotation transformation network for point cloud analysis, called RTN, which could reduce the RDF of input 3D objects to 0. The RTN could be seamlessly inserted into many existing DNNs for point cloud analysis. Extensive experimental results on 3D point cloud classification and segmentation tasks demonstrate that the proposed RTN could improve the performances of several state-of-the-art methods significantly.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 18 references that are in the map. First posted on arXiv 7 Jul 2021.
Builds on
15 of 18 references.
References · 15
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
- 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
- 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
- 2015Spatial Transformer Networkscited by 1,423
- 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
- 2018PointCNN: Convolution On X-Transformed Pointscited by 764
- 2018SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filterscited by 261
- 2019Grid-GCN for Fast and Scalable Point Cloud Learningcited by 103
- 2019Rotation Invariant Convolutions for 3D Point Clouds Deep Learningcited by 80
- 2019Effective Rotation-invariant Point CNN with Spherical Harmonics kernelscited by 57
- 20193D-Rotation-Equivariant Quaternion Neural Networkscited by 30
3 more
- 2019SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentationcited by 29
- 2018Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolutioncited by 16
- 2019Discrete Rotation Equivariance for Point Cloud Recognitioncited by 11
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.