Papers › CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence
CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence
Siyu Ren, Yiming Zeng, Junhui Hou, Xiaodong Chen
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image to Point Cloud Registration | KITTI | CorrI2P | RRE | 2.07 | #1 of 1 | Archive leaderboard | report |
| Image to Point Cloud Registration | KITTI | CorrI2P | RTE | 0.74 | #1 of 1 | Archive leaderboard | report |
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