AI 中文总结
研究图像到点云配准问题，提出交叉坐标对应关系修剪（CCP）策略，通过投影统一坐标、轻量级网络预测内点置信度及多密度点集成策略，提升配准精度，性能比现有方法至少高8.6%。
AI 中文摘要
最近的无检测方法通过采用从粗到细的匹配管道在图像到点云（I2P）配准中显示出显著效果。在粗阶段，通常融合下采样的图像特征和体素化的点云特征以建立初始粗对应关系以供后续细化。然而，现有方法很大程度上忽略了点云密度的关键作用，这从根本上决定了粗对应关系的质量和最终配准结果。具体而言，过于稀疏的点云导致内点数量不足，而过于密集的点云则常常引入高外点率。因此，这产生了固有的密度权衡，从而显著限制了当前方法的配准精度。为了减轻这种权衡，我们提出了一种新颖的交叉坐标对应关系修剪（CCP）策略，以在确保低外点率的同时获得足够的内点。为了最小化跨模态坐标差异的干扰，我们首先将交叉坐标粗对应关系投影到二维图像坐标系中进行空间统一。随后，一个轻量级修剪网络负责从坐标几何和模态特征维度预测内点置信度，并用于过滤粗外点。为了最大化内点召回率，我们进一步设计了一种多密度点集成（MDPE）策略，该策略在不同的点云密度上合并和去重修剪后的粗对应关系。我们的方法实现了显著的性能提升，在各种基准测试中的配准召回率上比现有最先进方法至少提高了8.6%。
英文摘要
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.
