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  4. Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding
 
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January 11, 2023
Paper (Preprint, Research Paper, Review Paper, White Paper, etc.)
Title

Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding

Title Supplement
Published on arXiv
Abstract
We present an improved approach for 3D object detection in point cloud data based on the Frustum PointNet (F-PointNet). Compared to the original F-PointNet, our newly proposed method considers the point neighborhood when computing point features. The newly introduced local neighborhood embedding operation mimics the convolutional operations in 2D neural networks. Thus features of each point are not only computed with the features of its own or of the whole point cloud but also computed especially with respect to the features of its neighbors. Experiments show that our proposed method achieves better performance than the F-Pointnet baseline on 3D object detection tasks.
Author(s)
Wu, Chengzhi
sl-0
Pfrommer, Julius  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Beyerer, Jürgen  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Li, Kangning
sl-0
Neubert, Boris
sl-0
DOI
10.48550/arXiv.2301.04613
Language
English
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Keyword(s)
  • 3D point clouds

  • object detection

  • deep learning

  • KNN-based embedding

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