BMFN3D: Bidirectional multilayer fusion network for indoor 3D object detection-Reference-Cited by-同舟云学术

BMFN3D: Bidirectional multilayer fusion network for indoor 3D object detection

Published:2022-07-11 Issue:18 Volume:58 Page:696-698
ISSN:0013-5194
Container-title:Electronics Letters
language:en
Short-container-title:Electronics Letters

Author:

Cheng Jun¹,Zhang Sheng¹²^ORCID

Affiliation:

1. CAS Key Laboratory of Human‐Machine Intelligence‐Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen People's Republic of China

2. Department of Artificial Intelligence and Automation School of Electrical and Automation Wuhan University Wuhan People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/ell2.12572

Reference21 articles.

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2. Qi C.R. et al.:PointNet: deep learning on point sets for 3D classification and segmentation. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp.652–660.IEEE Piscataway NJ(2017)

3. Cheng B. et al.:Back‐tracing representative points for voting‐based 3D object detection in point clouds. In:Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp.8963–8972.IEEE Piscataway NJ(2021)

4. Xie Q. et al.:MLCVNet: Multi‐level context votenet for 3d object detection. In:Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp.10447–10456.IEEE Piscataway NJ(2020)

5. Zhang Z. et al.:H3DNet: 3D object detection using hybrid geometric primitives. In:European Conference on Computer Vision pp.311–329.Springer Cham(2020)