Quantity-Aware Coarse-to-Fine Correspondence for Image-to-Point Cloud Registration

• URL: http://arxiv.org/abs/2307.07142v2
• Date: Thu, 18 Jan 2024 11:30:47 GMT
• Title: Quantity-Aware Coarse-to-Fine Correspondence for Image-to-Point Cloud Registration
• Authors: Gongxin Yao, Yixin Xuan, Yiwei Chen and Yu Pan
• Abstract summary: Image-to-point cloud registration aims to determine the relative camera pose between an RGB image and a reference point cloud. Matching individual points with pixels can be inherently ambiguous due to modality gaps. We propose a framework to capture quantity-aware correspondences between local point sets and pixel patches.
• Score: 4.954184310509112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-to-point cloud registration aims to determine the relative camera pose between an RGB image and a reference point cloud, serving as a general solution for locating 3D objects from 2D observations. Matching individual points with pixels can be inherently ambiguous due to modality gaps. To address this challenge, we propose a framework to capture quantity-aware correspondences between local point sets and pixel patches and refine the results at both the point and pixel levels. This framework aligns the high-level semantics of point sets and pixel patches to improve the matching accuracy. On a coarse scale, the set-to-patch correspondence is expected to be influenced by the quantity of 3D points. To achieve this, a novel supervision strategy is proposed to adaptively quantify the degrees of correlation as continuous values. On a finer scale, point-to-pixel correspondences are refined from a smaller search space through a well-designed scheme, which incorporates both resampling and quantity-aware priors. Particularly, a confidence sorting strategy is proposed to proportionally select better correspondences at the final stage. Leveraging the advantages of high-quality correspondences, the problem is successfully resolved using an efficient Perspective-n-Point solver within the framework of random sample consensus (RANSAC). Extensive experiments on the KITTI Odometry and NuScenes datasets demonstrate the superiority of our method over the state-of-the-art methods.

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