Title: SimTrack3D: A Simple Sequential Motion Modeling for Efficient 3D Single Object Tracking
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: 3D single object tracking, State Space Model, Point Cloud

Abstract: Accurate tracking of objects in 3D point clouds requires continuous and efficient motion modeling across spatial and temporal dimensions. Although voxel-based methods have recently achieved strong performance thanks to rich BEV representations, they inevitably introduce redundancy, thereby complicating motion extraction.Building on efficient point representations and their sequential modeling, we venture that voxel features can be reformulated as a state-sequence paradigm, serving as an intermediate representation for more effective motion modeling.To this end, we introduce a serialized motion modeling framework that sequentializes BEV features by embedding spatial positions within a structured voxel grid, naturally enabling more efficient processing.At its core is a simultaneous spatiotemporal scanning mechanism that enforces causal inference, preserves structural priors, and jointly disentangles motion features across adjacent domains.By leveraging geometric priors with a compact yet precise representation, our framework leads to significantly reduced computational overhead while enhancing tracking performance.  Interestingly, when integrated our approach with point-based methods, it further boosts performance through reinforced spatial modeling with minimal extra cost. Our method sets new SOTA records on the KITTI and NuScenes datasets, excelling in both accuracy and efficiency. Running at nearly 188 FPS on a single RTX 4090 GPU, it achieves a +19\% speed improvement over the current best counterpart.

TL;DR: Employing serialization processing techniques SSM to handle point cloud data and applying spatiotemporal scanning to construct efficient motion modeling.