去除交叉注意力提升激光雷达点云配准泛化能力
Unlocking Generalization Power in LiDAR Point Cloud Registration
- 摒弃交叉注意力，专注帧内特征提取
- 跨距离/数据集测试中召回率超90%
- 适合自动驾驶等需要强泛化的场景
在真实环境中，具备跨距离和跨数据集鲁棒泛化能力的激光雷达点云配准方法对自动驾驶等应用的安全性至关重要。现有方法难以实现这一目标。为此，我们提出UGP框架，通过移除交叉注意力机制来增强泛化性能，使网络更专注于帧内特征提取。同时引入渐进式自注意力模块以降低大场景中的模糊性，并融合鸟瞰图（BEV）特征以加入场景语义信息。在多个室外场景的泛化实验中验证了该方法的有效性：在KITTI和nuScenes上的跨距离泛化实验中，平均配准召回率分别达到94.5%和91.4%；在nuScenes到KITTI的跨数据集泛化中，平均召回率达90.9%。代码将开源。
原文摘要 · Abstract (English)
In real-world environments, a LiDAR point cloud registration method with robust generalization capabilities (across varying distances and datasets) is crucial for ensuring safety in autonomous driving and other LiDAR-based applications. However, current methods fall short in achieving this level of generalization. To address these limitations, we propose UGP, a pruned framework designed to enhance generalization power for LiDAR point cloud registration. The core insight in UGP is the elimination of cross-attention mechanisms to improve generalization, allowing the network to concentrate on intra-frame feature extraction. Additionally, we introduce a progressive self-attention module to reduce ambiguity in large-scale scenes and integrate Bird's Eye View (BEV) features to incorporate semantic information about scene elements. Together, these enhancements significantly boost the network's generalization performance. We validated our approach through various generalization experiments in multiple outdoor scenes. In cross-distance generalization experiments on KITTI and nuScenes, UGP achieved state-of-the-art mean Registration Recall rates of 94.5% and 91.4%, respectively. In cross-dataset generalization from nuScenes to KITTI, UGP achieved a state-of-the-art mean Registration Recall of 90.9%. Code will be available at https://github.com/peakpang/UGP.
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