P2-net: Joint description and detection of local features for pixel and point matching

Bing Wang, Changhao Chen, Zhaopeng Cui, Jie Qin, Chris Xiaoxuan Lu, Zhengdi Yu, Peijun Zhao, Zhen Dong, Fan Zhu, Niki Trigoni

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Accurately describing and detecting 2D and 3D key-points is crucial to establishing correspondences across images and point clouds. Despite a plethora of learning-based 2D or 3D local feature descriptors and detectors having been proposed, the derivation of a shared descriptor and joint keypoint detector that directly matches pixels and points remains under-explored by the community. This work takes the initiative to establish fine-grained correspondences between 2D images and 3D point clouds. In order to directly match pixels and points, a dual fully-convolutional framework is presented that maps 2D and 3D inputs into a shared latent representation space to simultaneously describe and detect keypoints. Furthermore, an ultra-wide reception mechanism and a novel loss function are designed to mitigate the intrinsic information variations between pixel and point local regions. Extensive experimental results demonstrate that our framework shows competitive performance in fine-grained matching between images and point clouds and achieves state-of-the-art results for the task of indoor visual localization. Our source code is available at https://github.com/BingCS/P2-Net.
Original languageEnglish
Title of host publicationProceedings of the IEEE/CVF International Conference on Computer Vision
Number of pages10
ISBN (Electronic)978-1-6654-2812-5
ISBN (Print)978-1-6654-2813-2
Publication statusPublished - 28 Feb 2022
EventInternational Conference on Computer Vision 2021 - Online
Duration: 11 Oct 202117 Oct 2021

Publication series

Name2021 IEEE/CVF International Conference on Computer Vision (ICCV)
ISSN (Print)1550-5499
ISSN (Electronic)2380-7504


ConferenceInternational Conference on Computer Vision 2021
Abbreviated titleICCV 2021
Internet address


  • Vision for robotics and autonomous vehicles
  • Vision applications and systems


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