The KITTI Vision Benchmark Suite

We introduce S3MOT, a Selective State Space model-
based MOT method that efficiently infers 3D motion
and object associations from 2D images through three
core components: (i) Fully Convolutional, One-stage
Embedding (FCOE), which uses dense feature maps for
contrastive learning to enhance the representational
robustness of extracted Re-ID features, mitigating
challenges from occlusions and perspective
variations; (ii) VeloSSM, a specialized SSM-based
encoder-decoder structure, addresses scale
inconsistency and refines motion predictions by
modeling temporal dependencies in velocity dynamics;
and (iii) Hungarian State Space Model (HSSM), which
employs input-adaptive spatiotemporal scanning and
merging, grounded in SSM principles, to associate
diverse tracking cues efficiently and ensure
reliable tracklet-detection assignments

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@article{yan2025s3mot,
title={S3MOT: Monocular 3D Object Tracking with
Selective State Space Model},
author={Yan, Zhuohao and Feng, Shaoquan and Li,
Xingxing and Zhou, Yuxuan and Xia, Chunxi and Li,
Shengyu},
journal={arXiv preprint arXiv:2504.18068},
year={2025}
}