3D single object tracking (SOT) is a crucial task in fields of mobile
robotics and autonomous driving. Traditional motion-based approaches achieve
target tracking by estimating the relative movement of target between two
consecutive frames. However, they usually overlook local motion information of
the target and fail to exploit historical frame information effectively. To
overcome the above limitations, we propose a point-level flow method with
multi-frame information for 3D SOT task, called FlowTrack. Specifically, by
estimating the flow for each point in the target, our method could capture the
local motion details of target, thereby improving the tracking performance. At
the same time, to handle scenes with sparse points, we present a learnable
target feature as the bridge to efficiently integrate target information from
past frames. Moreover, we design a novel Instance Flow Head to transform dense
point-level flow into instance-level motion, effectively aggregating local
motion information to obtain global target motion. Finally, our method achieves
competitive performance with improvements of 5.9% on the KITTI dataset and 2.9%
on NuScenes. The code will be made publicly available soon.

提出了一种用于 3D 单目标跟踪任务的点级流方法，命名为 FlowTrack，通过估计目标中的每个点的流，能够捕捉目标的局部运动细节，提高跟踪性能，并有效地汇聚局部运动信息来获取全局目标运动。在 KITTI 数据集上有 5.9% 的性能提升，在 NuScenes 数据集上有 2.9% 的性能提升。