用于图像到点云配准的数量感知由粗到细对应方法
Quantity-Aware Coarse-to-Fine Correspondence for Image-to-Point Cloud Registration
AI总结：
针对图像与点云跨模态匹配歧义问题，提出数量感知由粗到细对应框架，通过连续相关性监督、重采样先验和置信度排序提升配准精度，并在KITTI Odometry与NuScenes上优于现有方法。
摘要：
图像到点云配准旨在确定RGB图像与参考点云之间的相对相机位姿，是从2D观测中定位3D物体的通用解决方案。由于模态差异，将单个点与像素进行匹配可能具有内在的歧义性。为应对这一挑战，我们提出了一个框架，用于捕获局部点集与像素块之间的数量感知对应关系，并在点和像素两个层级上细化结果。该框架对齐点集与像素块的高层语义，以提高匹配精度。在粗尺度上，集到块的对应关系预计会受到3D点数量的影响。为此，本文提出一种新颖的监督策略，将相关程度自适应地量化为连续值。在更细尺度上，通过精心设计的方案从更小的搜索空间中细化点到像素的对应关系，该方案同时结合了重采样和数量感知先验。特别是，本文提出一种置信度排序策略，在最终阶段按比例选择更好的对应关系。利用高质量对应关系的优势，该问题在随机样本一致性（RANSAC）框架内通过高效的Perspective-n-Point求解器得到成功解决。在KITTI Odometry和NuScenes数据集上的大量实验表明，我们的方法优于最先进的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
