Abstract / Description of output
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 language | English |
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Title of host publication | Proceedings of the IEEE/CVF International Conference on Computer Vision |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 15984-15993 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-6654-2812-5 |
ISBN (Print) | 978-1-6654-2813-2 |
DOIs | |
Publication status | Published - 28 Feb 2022 |
Event | International Conference on Computer Vision 2021 - Online Duration: 11 Oct 2021 → 17 Oct 2021 https://iccv2021.thecvf.com/home |
Publication series
Name | 2021 IEEE/CVF International Conference on Computer Vision (ICCV) |
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Publisher | IEEE |
ISSN (Print) | 1550-5499 |
ISSN (Electronic) | 2380-7504 |
Conference
Conference | International Conference on Computer Vision 2021 |
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Abbreviated title | ICCV 2021 |
Period | 11/10/21 → 17/10/21 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- Vision for robotics and autonomous vehicles
- Vision applications and systems