Rotation invariant convolutions for 3D point clouds deep learning
Recent progresses in 3D deep learning has shown that it is possible to design special convolution operators to consume point cloud data. However, a typical drawback is that rotation invariance is often not guaranteed, resulting in networks that generalizes poorly to arbitrary rotations. In this pape...
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sg-smu-ink.sis_research-89452023-07-20T07:49:20Z Rotation invariant convolutions for 3D point clouds deep learning ZHANG, Zhiyuan HUA, Binh-Son ROSEN, David W. YEUNG, Sai-Kit Recent progresses in 3D deep learning has shown that it is possible to design special convolution operators to consume point cloud data. However, a typical drawback is that rotation invariance is often not guaranteed, resulting in networks that generalizes poorly to arbitrary rotations. In this paper, we introduce a novel convolution operator for point clouds that achieves rotation invariance. Our core idea is to use low-level rotation invariant geometric features such as distances and angles to design a convolution operator for point cloud learning. The well-known point ordering problem is also addressed by a binning approach seamlessly built into the convolution. This convolution operator then serves as the basic building block of a neural network that is robust to point clouds under 6-DoF transformations such as translation and rotation. Our experiment shows that our method performs with high accuracy in common scene understanding tasks such as object classification and segmentation. Compared to previous and concurrent works, most importantly, our method is able to generalize and achieve consistent results across different scenarios in which training and testing can contain arbitrary rotations. Our implementation is publicly available at our project page. 2019-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7942 info:doi/10.1109/3dv.2019.00031 https://ink.library.smu.edu.sg/context/sis_research/article/8945/viewcontent/313100a204.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Three-dimensional displays Feature extraction Deep learning Task analysis Neural networks Training Semantics Graphics and Human Computer Interfaces OS and Networks |
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Three-dimensional displays Feature extraction Deep learning Task analysis Neural networks Training Semantics Graphics and Human Computer Interfaces OS and Networks ZHANG, Zhiyuan HUA, Binh-Son ROSEN, David W. YEUNG, Sai-Kit Rotation invariant convolutions for 3D point clouds deep learning |
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Recent progresses in 3D deep learning has shown that it is possible to design special convolution operators to consume point cloud data. However, a typical drawback is that rotation invariance is often not guaranteed, resulting in networks that generalizes poorly to arbitrary rotations. In this paper, we introduce a novel convolution operator for point clouds that achieves rotation invariance. Our core idea is to use low-level rotation invariant geometric features such as distances and angles to design a convolution operator for point cloud learning. The well-known point ordering problem is also addressed by a binning approach seamlessly built into the convolution. This convolution operator then serves as the basic building block of a neural network that is robust to point clouds under 6-DoF transformations such as translation and rotation. Our experiment shows that our method performs with high accuracy in common scene understanding tasks such as object classification and segmentation. Compared to previous and concurrent works, most importantly, our method is able to generalize and achieve consistent results across different scenarios in which training and testing can contain arbitrary rotations. Our implementation is publicly available at our project page. |
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ZHANG, Zhiyuan HUA, Binh-Son ROSEN, David W. YEUNG, Sai-Kit |
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ZHANG, Zhiyuan HUA, Binh-Son ROSEN, David W. YEUNG, Sai-Kit |
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ZHANG, Zhiyuan |
title |
Rotation invariant convolutions for 3D point clouds deep learning |
title_short |
Rotation invariant convolutions for 3D point clouds deep learning |
title_full |
Rotation invariant convolutions for 3D point clouds deep learning |
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Rotation invariant convolutions for 3D point clouds deep learning |
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Rotation invariant convolutions for 3D point clouds deep learning |
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rotation invariant convolutions for 3d point clouds deep learning |
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Institutional Knowledge at Singapore Management University |
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2019 |
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https://ink.library.smu.edu.sg/sis_research/7942 https://ink.library.smu.edu.sg/context/sis_research/article/8945/viewcontent/313100a204.pdf |
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