A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation
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Date
2018Type
- Conference Paper
Citations
Cited 34 times in
Web of Science
Cited 37 times in
Scopus
ETH Bibliography
yes
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Abstract
We propose a novel neural network architecture for point cloud classification. Our key idea is to automatically transform the 3D unordered input data into a set of useful 2D depth images, and classify them by exploiting well performing image classification CNNs. We present new differentiable module designs to generate depth images from a point cloud. These modules can be combined with any network architecture for processing point clouds. We utilize them in combination with state-of-the-art classification networks, and get results competitive with the state of the art in point cloud classification. Furthermore, our architecture automatically produces informative images representing the input point cloud, which could be used for further applications such as point cloud visualization. Show more
Publication status
publishedExternal links
Book title
2018 IEEE/CVF Conference on Computer Vision and Pattern RecognitionPages / Article No.
Publisher
IEEEEvent
Organisational unit
03420 - Gross, Markus / Gross, Markus
Funding
146227 - Analysis, Reconstruction and Processing of Non-manifold Point-sampled Geometry (SNF)
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Show all metadata
Citations
Cited 34 times in
Web of Science
Cited 37 times in
Scopus
ETH Bibliography
yes
Altmetrics