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.

3D单目标跟踪是计算机视觉的前沿任务之一，本研究提出了一种基于点云的全新3D单目标跟踪框架M3SOT，通过多个输入帧、多个感受野和多个解决空间的协同作用，从点云中直接建模时间性、上下文信息和任务，以实现高效的目标跟踪应用。M3SOT的实验表明，在KITTI、nuScenes和Waymo Open Dataset等基准上，其性能达到了最先进的水平，帧率达到了38帧每秒。

M3SOT：多帧、多场、多空间的 3D 单目标跟踪