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

Fraunhofer IOSB; Wu, Chengzhi; Pfrommer, Julius; Beyerer, Jürgen; Li, Kangning; Neubert, Boris

We present an improved approach for 3D object detection in point clouds 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.

Postprint §
DOI: 10.5445/IR/1000126362
Veröffentlicht am 23.04.2021
DOI: 10.1109/ICIEVicIVPR48672.2020.9306522
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Institut für Visualisierung und Datenanalyse (IVD)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator ISBN: 978-1-7281-9332-8
KITopen-ID: 1000126362
Erschienen in 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan, 26-29 Aug. 2020
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Vorab online veröffentlicht am 07.01.2021
Schlagwörter 3D point clouds, object detection, deep learning, KNN-based embedding
Nachgewiesen in Dimensions
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