  Considering the complementarity of scene flow estimation in the spatial
domain's focusing capability and 3D object tracking in the temporal domain's
coherence, this study aims to address a comprehensive new task that can
simultaneously capture fine-grained and long-term 3D motion in an online
manner: long-term scene flow estimation (LSFE). We introduce SceneTracker, a
novel learning-based LSFE network that adopts an iterative approach to
approximate the optimal trajectory. Besides, it dynamically indexes and
constructs appearance and depth correlation features simultaneously and employs
the Transformer to explore and utilize long-range connections within and
between trajectories. With detailed experiments, SceneTracker shows superior
capabilities in handling 3D spatial occlusion and depth noise interference,
highly tailored to the LSFE task's needs. Finally, we build the first
real-world evaluation dataset, LSFDriving, further substantiating
SceneTracker's commendable generalization capacity. The code and data for
SceneTracker is available at https://github.com/wwsource/SceneTracker.
