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3D single object tracking plays a crucial role in computer vision. Mainstreammethods mainly rely on point clouds to achieve geometry matching between targettemplate and search area. However, textureless and incomplete point clouds makeit difficult for single-modal trackers to distinguish objects with similarstructures. To overcome the limitations of geometry matching, we propose aMulti-modal Multi-level Fusion Tracker (MMF-Track), which exploits the imagetexture and geometry characteristic of point clouds to track 3D target.Specifically, we first propose a Space Alignment Module (SAM) to align RGBimages with point clouds in 3D space, which is the prerequisite forconstructing inter-modal associations. Then, in feature interaction level, wedesign a Feature Interaction Module (FIM) based on dual-stream structure, whichenhances intra-modal features in parallel and constructs inter-modal semanticassociations. Meanwhile, in order to refine each modal feature, we introduce aCoarse-to-Fine Interaction Module (CFIM) to realize the hierarchical featureinteraction at different scales. Finally, in similarity fusion level, wepropose a Similarity Fusion Module (SFM) to aggregate geometry and textureclues from the target. Experiments show that our method achievesstate-of-the-art performance on KITTI (39% Success and 42% Precision gainsagainst previous multi-modal method) and is also competitive on NuScenes.

MMF-Track: Multi-modal Multi-level Fusion for 3D Single Object Tracking