通过智能选参与交互提升图像点云配准鲁棒性
Adaptive Agent Selection and Interaction Network for Image-to-point cloud Registration
- 用强化学习动态选择可靠特征代理，增强结构感知
- 在RGB-D Scenes v2和7-Scenes上达到当前最优性能
- 适合需要高精度配准的自动驾驶与AR应用
无检测的图像到点云配准方法通常依赖基于Transformer的架构来聚合跨模态特征并建立对应关系。然而，在噪声干扰下，相似性计算易出错，导致错误对应。此外，缺乏专门设计使得跨模态中有效信息的表示选择困难，限制了配准的鲁棒性和准确性。为此，我们提出一种新型跨模态配准框架，包含两个核心模块：迭代代理选择（IAS）模块和可靠代理交互（RAI）模块。IAS通过相位图增强结构特征感知，并采用强化学习原则高效选择可靠代理；RAI则利用所选代理引导跨模态交互，有效减少误匹配，提升整体鲁棒性。在RGB-D Scenes v2和7-Scenes基准上的大量实验表明，该方法持续取得当前最优性能。
原文摘要 · Abstract (English)
Typical detection-free methods for image-to-point cloud registration leverage transformer-based architectures to aggregate cross-modal features and establish correspondences. However, they often struggle under challenging conditions, where noise disrupts similarity computation and leads to incorrect correspondences. Moreover, without dedicated designs, it remains difficult to effectively select informative and correlated representations across modalities, thereby limiting the robustness and accuracy of registration. To address these challenges, we propose a novel cross-modal registration framework composed of two key modules: the Iterative Agents Selection (IAS) module and the Reliable Agents Interaction (RAI) module. IAS enhances structural feature awareness with phase maps and employs reinforcement learning principles to efficiently select reliable agents. RAI then leverages these selected agents to guide cross-modal interactions, effectively reducing mismatches and improving overall robustness. Extensive experiments on the RGB-D Scenes v2 and 7-Scenes benchmarks demonstrate that our method consistently achieves state-of-the-art performance.
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