Title | ||
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P2-Net - Joint Description and Detection of Local Features for Pixel and Point Matching. |
Abstract | ||
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Accurately describing and detecting 2D and 3D keypoints 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 in combination with 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 will be available at [no-name-for-blind-review]. |
Year | DOI | Venue |
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2021 | 10.1109/ICCV48922.2021.01570 | ICCV |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 11 |
Name | Order | Citations | PageRank |
---|---|---|---|
Bing Wang | 1 | 6 | 2.47 |
Changhao Chen | 2 | 27 | 8.71 |
Zhaopeng Cui | 3 | 93 | 16.66 |
Jie Qin | 4 | 0 | 0.34 |
Chris Xiaoxuan Lu | 5 | 27 | 13.62 |
Zhengdi Yu | 6 | 0 | 0.34 |
Peijun Zhao | 7 | 46 | 8.22 |
Zhen Dong | 8 | 0 | 0.34 |
Fan Zhu | 9 | 0 | 0.68 |
Niki Trigoni | 10 | 1160 | 85.23 |
Andrew Markham | 11 | 519 | 48.34 |