P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching

• URL: http://arxiv.org/abs/2103.01055v1
• Date: Mon, 1 Mar 2021 14:59:40 GMT
• Title: P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching
• Authors: Bing Wang, Changhao Chen, Zhaopeng Cui, Jie Qin, Chris Xiaoxuan Lu, Zhengdi Yu, Peijun Zhao, Zhen Dong, Fan Zhu, Niki Trigoni, Andrew Markham
• Abstract summary: This work takes the initiative to establish fine-grained correspondences between 2D images and 3D point clouds. 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.
• Score: 78.18641868402901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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].

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