CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence

• URL: http://arxiv.org/abs/2207.05483v1
• Date: Tue, 12 Jul 2022 11:49:31 GMT
• Title: CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence
• Authors: Siyu Ren, Yiming Zeng, Junhui Hou and Xiaodong Chen
• Abstract summary: We propose the first feature-based dense correspondence framework for addressing the image-to-point cloud registration problem, dubbed CorrI2P. Specifically, given a pair of a 2D image before a 3D point cloud, we first transform them into high-dimensional feature space feed the features into a symmetric overlapping region to determine the region where the image point cloud overlap.
• Score: 51.91791056908387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motivated by the intuition that the critical step of localizing a 2D image in the corresponding 3D point cloud is establishing 2D-3D correspondence between them, we propose the first feature-based dense correspondence framework for addressing the image-to-point cloud registration problem, dubbed CorrI2P, which consists of three modules, i.e., feature embedding, symmetric overlapping region detection, and pose estimation through the established correspondence. Specifically, given a pair of a 2D image and a 3D point cloud, we first transform them into high-dimensional feature space and feed the resulting features into a symmetric overlapping region detector to determine the region where the image and point cloud overlap each other. Then we use the features of the overlapping regions to establish the 2D-3D correspondence before running EPnP within RANSAC to estimate the camera's pose. Experimental results on KITTI and NuScenes datasets show that our CorrI2P outperforms state-of-the-art image-to-point cloud registration methods significantly. We will make the code publicly available.

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