Spatio-Temporal Bi-directional Cross-frame Memory for Distractor
  Filtering Point Cloud Single Object Tracking
Shaoyu Sun Chunyang Wang Xuelian Liu Chunhao Shi Yueyang Ding Guan Xi
3D single object tracking within LIDAR point clouds is a pivotal task in
computer vision, with profound implications for autonomous driving and
robotics. However, existing methods, which depend solely on appearance matching
via Siamese networks or utilize motion information from successive frames,
encounter significant challenges. Issues such as similar objects nearby or
occlusions can result in tracker drift. To mitigate these challenges, we design
an innovative spatio-temporal bi-directional cross-frame distractor filtering
tracker, named STMD-Tracker. Our first step involves the creation of a 4D
multi-frame spatio-temporal graph convolution backbone. This design separates
KNN graph spatial embedding and incorporates 1D temporal convolution,
effectively capturing temporal fluctuations and spatio-temporal information.
Subsequently, we devise a novel bi-directional cross-frame memory procedure.
This integrates future and synthetic past frame memory to enhance the current
memory, thereby improving the accuracy of iteration-based tracking. This
iterative memory update mechanism allows our tracker to dynamically compensate
for information in the current frame, effectively reducing tracker drift.
Lastly, we construct spatially reliable Gaussian masks on the fused features to
eliminate distractor points. This is further supplemented by an object-aware
sampling strategy, which bolsters the efficiency and precision of object
localization, thereby reducing tracking errors caused by distractors. Our
extensive experiments on KITTI, NuScenes and Waymo datasets demonstrate that
our approach significantly surpasses the current state-of-the-art methods.