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3D Single Object Tracking (SOT) stands a forefront task of computer vision,proving essential for applications like autonomous driving. Sparse and occludeddata in scene point clouds introduce variations in the appearance of trackedobjects, adding complexity to the task. In this research, we unveil M3SOT, anovel 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 modelingtemporality, contexts, and tasks directly from point clouds, revisiting aperspective on the key factors influencing SOT. To this end, we design atransformer-based network centered on point cloud targets in the search area,aggregating diverse contextual representations and propagating target cues byemploying historical frames. As M3SOT spans varied processing perspectives,we've streamlined the network-trimming its depth and optimizing itsstructure-to ensure a lightweight and efficient deployment for SOTapplications. We posit that, backed by practical construction, M3SOT sidestepsthe need for complex frameworks and auxiliary components to deliver sterlingresults. Extensive experiments on benchmarks such as KITTI, nuScenes, and WaymoOpen Dataset demonstrate that M3SOT achieves state-of-the-art performance at 38FPS. Our code and models are available athttps://github.com/ywu0912/TeamCode.git.

M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object Tracking