Learning robust feature matching between the template and search area is crucial for 3D Siamese tracking. The core of Siamese feature matching is how to assign high feature similarity on the corresponding points between the template and search area for precise object localization. In this paper, we propose a novel point cloud registration-driven Siamese tracking framework, with the intuition that spatially aligned corresponding points (via 3D registration) tend to achieve consistent feature representations. Specifically, our method consists of two modules, including a tracking-specific nonlocal registration module and a registration-aided Sinkhorn template-feature aggregation module. The registration module targets at the precise spatial alignment between the template and search area. The tracking-specific spatial distance constraint is proposed to refine the cross-attention weights in the nonlocal module for discriminative feature learning. Then, we use the weighted SVD to compute the rigid transformation between the template and search area, and align them to achieve the desired spatially aligned corresponding points. For the feature aggregation model, we formulate the feature matching between the transformed template and search area as an optimal transport problem and utilize the Sinkhorn optimization to search for the outlier-robust matching solution. Also, a registration-aided spatial distance map is built to improve the matching robustness in indistinguishable regions (e.g., smooth surface). Finally, guided by the obtained feature matching map, we aggregate the target information from the template into the search area to construct the target-specific feature, which is then fed into a CenterPoint-like detection head for object localization. Extensive experiments on KITTI, NuScenes and Waymo datasets verify the effectiveness of our proposed method.
翻译:模板和搜索区域之间的学习强度匹配功能对于 3D siames 跟踪 3D siames 模板和搜索区域至关重要。 Siames 特征匹配的核心在于如何在模板和搜索区域之间的对应点上给模板和搜索目标定位之间的对应点指定高度相似性。 在本文中,我们提议了一个全新的点云登记驱动的Siames 跟踪框架, 其直觉是空间对齐对应点( 通过 3D 登记) 往往达到一致的特征表示。 具体地说, 我们的方法由两个模块组成, 包括一个跟踪非本地登记模块和一个注册辅助的 Sinkhorn 模板组合组合模块。 注册模块在模板和搜索区域之间的精确空间匹配目标。 跟踪特定的空间距离限制是改进非本地模块的交叉关注权, 用于区别特性学习。 然后, 我们使用加权的 SVD 来将模板和搜索区域之间的僵硬度转换, 并调整它们实现所需的空间对齐对应点 。 对于特性汇总模型模型, 我们将转换模板和搜索区域之间的功能匹配功能匹配, 将Sinkhackhorn 优化到远程搜索区域, 匹配到远程校正路路标 。