SpatialTrackerV2：让3D点跟踪变得简单
SpatialTrackerV2: 3D Point Tracking Made Easy
AI总结：
SpatialTrackerV2提出端到端前馈式3D点跟踪方法，联合学习场景几何、相机位姿与物体运动，在异构数据上训练后性能超越现有方法30%，速度比动态3D重建快50倍。
摘要：
我们提出SpatialTrackerV2，一种用于单目视频的前馈式3D点跟踪方法。不同于基于现成组件构建的模块化3D跟踪流程，我们的方法将点跟踪、单目深度和相机位姿估计之间的内在联系统一到一个高性能的前馈式3D点跟踪器中。它将世界空间中的3D运动分解为场景几何、相机自运动和像素级物体运动，并采用完全可微的端到端架构，从而能够在广泛的数据集上进行可扩展训练，包括合成序列、带位姿的RGB-D视频以及无标注的野外实拍素材。通过从这些异构数据中联合学习几何与运动，SpatialTrackerV2的性能比现有3D跟踪方法高出30%，并且达到领先的动态3D重建方法的精度，同时运行速度快50倍。
英文摘要：
We present SpatialTrackerV2, a feed-forward 3D point tracking method for monocular videos. Going beyond modular pipelines built on off-the-shelf components for 3D tracking, our approach unifies the intrinsic connections between point tracking, monocular depth, and camera pose estimation into a high-performing and feedforward 3D point tracker. It decomposes world-space 3D motion into scene geometry, camera ego-motion, and pixel-wise object motion, with a fully differentiable and end-to-end architecture, allowing scalable training across a wide range of datasets, including synthetic sequences, posed RGB-D videos, and unlabeled in-the-wild footage. By learning geometry and motion jointly from such heterogeneous data, SpatialTrackerV2 outperforms existing 3D tracking methods by 30%, and matches the accuracy of leading dynamic 3D reconstruction approaches while running 50$\times$ faster.
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