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  PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection

Shi, S., Jiang, L., Deng, J., Wang, Z., Guo, C., Shi, J., et al. (2022). PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection. International Journal of Computer Vision, 131, 531-551. doi:10.1007/s11263-022-01710-9.

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Genre: Journal Article
Latex : {PV-RCNN}++: {P}oint-Voxel Feature Set Abstraction With Local Vector Representation for {3D} Object Detection

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adap- tation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indi- cate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copy- right holder. To view a copy of this licence, visit

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 Creators:
Shi, Shaoshuai1, Author           
Jiang, Li1, Author           
Deng, Jiajun2, Author
Wang, Zhe2, Author
Guo, Chaoxu2, Author
Shi, Jianping2, Author
Wang, Xiaogang2, Author
Li, Hongsheng2, Author
Affiliations:
1Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 2022
 Publication Status: Published online
 Pages: 21 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/s11263-022-01710-9
BibTex Citekey: Shi2022x
URI: https://rdcu.be/c14JE
 Degree: -

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Title: International Journal of Computer Vision
  Other : Int. J. Comput. Vis.
Source Genre: Journal
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Publ. Info: New York, NY : Springer
Pages: - Volume / Issue: 131 Sequence Number: - Start / End Page: 531 - 551 Identifier: ISSN: 0920-5691
CoNE: https://pure.mpg.de/cone/journals/resource/954925564668