3D single object tracking with LiDAR points is an important task in the
computer vision field. Previous methods usually adopt the matching-based or
motion-centric paradigms to estimate the current target status. However, the
former is sensitive to the similar distractors and the sparseness of point
cloud due to relying on appearance matching, while the latter usually focuses
on short-term motion clues (eg. two frames) and ignores the long-term motion
pattern of target. To address these issues, we propose a mixed paradigm with
two stages, named MTM-Tracker, which combines motion modeling with feature
matching into a single network. Specifically, in the first stage, we exploit
the continuous historical boxes as motion prior and propose an encoder-decoder
structure to locate target coarsely. Then, in the second stage, we introduce a
feature interaction module to extract motion-aware features from consecutive
point clouds and match them to refine target movement as well as regress other
target states. Extensive experiments validate that our paradigm achieves
competitive performance on large-scale datasets (70.9% in KITTI and 51.70% in
NuScenes). The code will be open soon at
this https URL

提出了一种混合模型，命名为 MTM-Tracker，将运动建模与特征匹配相结合，用于 3D 单物体追踪任务，并在大规模数据集上取得了竞争性能。