M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object Tracking
Jiaming Liu Yue Wu Maoguo Gong Qiguang Miao Wenping Ma Can Qin
3D Single Object Tracking (SOT) stands a forefront task of computer vision,
proving essential for applications like autonomous driving. Sparse and occluded
data in scene point clouds introduce variations in the appearance of tracked
objects, adding complexity to the task. In this research, we unveil M3SOT, a
novel 3D SOT framework, which synergizes multiple input frames (template sets),
multiple receptive fields (continuous contexts), and multiple solution spaces
(distinct tasks) in ONE model. Remarkably, M3SOT pioneers in modeling
temporality, contexts, and tasks directly from point clouds, revisiting a
perspective on the key factors influencing SOT. To this end, we design a
transformer-based network centered on point cloud targets in the search area,
aggregating diverse contextual representations and propagating target cues by
employing historical frames. As M3SOT spans varied processing perspectives,
we've streamlined the network-trimming its depth and optimizing its
structure-to ensure a lightweight and efficient deployment for SOT
applications. We posit that, backed by practical construction, M3SOT sidesteps
the need for complex frameworks and auxiliary components to deliver sterling
results. Extensive experiments on benchmarks such as KITTI, nuScenes, and Waymo
Open Dataset demonstrate that M3SOT achieves state-of-the-art performance at 38
FPS. Our code and models are available at
https://github.com/ywu0912/TeamCode.git.