论文/arXiv 2607.17200·3 个月前

用于图像到点云配准的跨坐标对应关系剪枝

针对I2P配准中点云密度权衡导致的粗匹配质量问题，提出CCP与MDPE，并在多个基准验证性能提升。

AI 整理

• 感知与导航
• 评测与安全

原文链接：Cross-Coordinate Correspondence Pruning for Image-to-Point Cloud Registration（arXiv 2607.17200 · 发布日期：2026-07-19）

Cross-Coordinate Correspondence Pruning for Image-to-Point Cloud Registration

针对I2P配准中点云密度权衡导致的粗匹配质量问题，提出CCP与MDPE，并在多个基准验证性能提升。

核心要点

• 现有无检测方法忽视点云密度对粗对应和最终配准结果的关键影响。
• CCP将跨坐标粗对应投影到2D图像坐标系，并用轻量网络预测内点置信度。
• MDPE整合并去重不同点云密度下剪枝后的粗对应，以提升内点召回。
• 在多个基准上，Registration Recall较现有最优方法至少提升8.6%。

验证范围与局限：验证范围为多个图像到点云配准基准，摘要未说明具体场景。

英文摘要 (Abstract)

Recent detection-free approaches have shown significant efficacy in image-to-point cloud (I2P) registration by employing a coarse-to-fine matching pipeline. In the coarse stage, down-sampled image features and voxelized point cloud features are typically fused to establish initial coarse correspondences for subsequent refinement. However, existing methods largely overlook the critical role of point cloud density, which fundamentally dictates the quality of coarse correspondences and the final registration results. Specifically, excessively sparse point clouds lead to an insufficient number of inliers, while overly dense ones often introduce a high outlier ratio. Consequently, this creates an inherent density trade-off, thereby significantly limiting the registration accuracy of current approaches. For mitigating this trade-off, we propose a novel Cross-Coordinate Correspondences Pruning (CCP) strategy to acquire sufficient inliers while ensuring a low outlier ratio. To minimize interference from inter-modal coordinate discrepancies, we first project cross-coordinate coarse correspondences to the 2D image coordinate system for spatial unification. Subsequently, a lightweight pruning network is responsible for predicting the inlier confidences, which are used to filter coarse outliers, from coordinate geometric and modal feature dimensions. To maximize inlier recall, we further design a Multi-Density Point Ensemble (MDPE) strategy that consolidates and deduplicates pruned coarse correspondences across varying point cloud densities. Our method achieves a significant performance improvement, surpassing existing state-of-the-art methods by at least 8.6% in Registration Recall across various benchmarks.

作者：Xin Liu, Rong Qin, Huipeng Lin, Leizhi Shu, Jin Wu, Chi‐Man Vong, L C-C Lin, Jufeng Yang

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2607.17200：Cross-Coordinate Correspondence Pruning for Image-to-Point Cloud Registration访问 2026-10-21