用角度一致性提升图像点云配准的抗噪能力
Angle-I2P: Angle-Consistent-Aware Hierarchical Attention for Cross-Modality Outlier Rejection
- 基于角度一致性设计跨模态几何约束
- 通过层级注意力将内点率提升至90%以上
- 适合低内点率场景的机器人视觉定位
图像到点云配准（I2P）是机器人操作、抓取和定位等应用中的基础任务。现有基于深度学习的方法在学习表征空间中对齐图像与点云特征以建立对应关系，已取得良好效果。然而当初始匹配对的内点率较低时，传统透视-多点（PnP）方法难以获得准确结果。为此，本文提出Angle-I2P，一种利用角度一致性几何约束与层级注意力的异常值剔除网络。首先，设计了一种尺度不变的跨模态几何约束，基于角度一致性，引导模型区分内点与外点。其次，提出全局到局部的层级注意力机制，在刚性变换下有效过滤几何不一致匹配，显著提升内点率（IR）与配准召回率（RR）。实验表明，该方法在7Scenes、RGBD Scenes V2及自建数据集上均达到领先性能，所有基准测试均有稳定提升。
原文摘要 · Abstract (English)
Image-to-point-cloud registration (I2P) is a fundamental task in robotic applications such as manipulation,grasping, and localization. Existing deep learning-based I2P methods seek to align image and point cloud features in a learned representation space to establish correspondences, and have achieved promising results. However, when the inlier ratio of the initial matching pairs is low, conventional Perspective-n-Points (PnP) methods may struggle to achieve accurate results. To address this limitation, we propose Angle-I2P, an outlier rejection network that leverages angle-consistent geometric constraints and hierarchical attention. First, we design a scale-invariant, crossmodality geometric constraint based on angular consistency. This explicit geometric constraint guides the model in distinguishing inliers from outliers. Furthermore, we propose a global-tolocal hierarchical attention mechanism that effectively filters out geometrically inconsistent matches under rigid transformation, thereby improving the Inlier Ratio (IR) and Registration Recall (RR). Experimental results demonstrate that our method achieves state-of-the-art performance on the 7Scenes, RGBD Scenes V2, and a self-collected dataset, with consistent improvements across all benchmarks.
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