# MVCTrack: Boosting 3D Point Cloud Tracking via Multimodal-Guided Virtual Cues arXiv: https://arxiv.org/abs/2412.02734 ยท HTML: https://mlsanity.com/paper/2412.02734/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 36 of its 44 references that are in the map. - First posted on arXiv 3 Dec 2024. ## Builds on (most cited first, 30 of 36) - [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019) - [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019) - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022) - [Deep Hough Voting for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1904.09664/) (2019) - [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016) - [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022) - [Multimodal Virtual Point 3D Detection](https://mlsanity.com/paper/2111.06881/) (2021) - [A Novel Performance Evaluation Methodology for Single-Target Trackers](https://mlsanity.com/paper/1503.01313/) (2015) - [P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds](https://mlsanity.com/paper/2005.13888/) (2020) - [EagerMOT: 3D Multi-Object Tracking via Sensor Fusion](https://mlsanity.com/paper/2104.14682/) (2021) - [Leveraging Shape Completion for 3D Siamese Tracking](https://mlsanity.com/paper/1903.01784/) (2019) - [Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds](https://mlsanity.com/paper/2108.04728/) (2021) - [PTTR: Relational 3D Point Cloud Object Tracking with Transformer](https://mlsanity.com/paper/2112.02857/) (2021) - [PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds](https://mlsanity.com/paper/2108.06455/) (2021) - [Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds](https://mlsanity.com/paper/2203.01730/) (2022) - [3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds](https://mlsanity.com/paper/2111.04426/) (2021) - [3D Object Tracking with Transformer](https://mlsanity.com/paper/2110.14921/) (2021) - [3D-SiamRPN: An End-to-End Learning Method for Real-Time 3D Single Object Tracking Using Raw Point Cloud](https://mlsanity.com/paper/2108.05630/) (2021) - [3D Siamese Transformer Network for Single Object Tracking on Point Clouds](https://mlsanity.com/paper/2207.11995/) (2022) - [FastPillars: A Deployment-friendly Pillar-based 3D Detector](https://mlsanity.com/paper/2302.02367/) (2023) - [Real-time 3D Single Object Tracking with Transformer](https://mlsanity.com/paper/2209.00860/) (2022) - [MBPTrack: Improving 3D Point Cloud Tracking with Memory Networks and Box Priors](https://mlsanity.com/paper/2303.05071/) (2023) - [A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds](https://mlsanity.com/paper/2203.04232/) (2022) - [GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds](https://mlsanity.com/paper/2211.10927/) (2022) - [Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer](https://mlsanity.com/paper/2208.05216/) (2022) - [OSP2B: One-Stage Point-to-Box Network for 3D Siamese Tracking](https://mlsanity.com/paper/2304.11584/) (2023) - [Synchronize Feature Extracting and Matching: A Single Branch Framework for 3D Object Tracking](https://mlsanity.com/paper/2308.12549/) (2023) - [Exploiting More Information in Sparse Point Cloud for 3D Single Object Tracking](https://mlsanity.com/paper/2210.00519/) (2022) - [An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds](https://mlsanity.com/paper/2303.12535/) (2023) - [Towards Category Unification of 3D Single Object Tracking on Point Clouds](https://mlsanity.com/paper/2401.11204/) (2024) ## Built on by (most cited first, 10 of 10) - [MonoFusion: Sparse-View 4D Reconstruction via Monocular Fusion](https://mlsanity.com/paper/2507.23782/) (2025) - [CompTrack: Information Bottleneck-Guided Low-Rank Dynamic Token Compression for Point Cloud Tracking](https://mlsanity.com/paper/2511.15580/) (2025) - [FocusTrack: One-Stage Focus-and-Suppress Framework for 3D Point Cloud Object Tracking](https://mlsanity.com/paper/2602.24133/) (2026) - [TiCAL:Typicality-Based Consistency-Aware Learning for Multimodal Emotion Recognition](https://mlsanity.com/paper/2511.15085/) (2025) - [Beyond Frame-wise Tracking: A Trajectory-based Paradigm for Efficient Point Cloud Tracking](https://mlsanity.com/paper/2509.11453/) (2025) - [TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking](https://mlsanity.com/paper/2609.07738/) (2026) - [FADE: Adversarial Concept Erasure in Flow Models](https://mlsanity.com/paper/2507.12283/) (2025) - [Robust Concept Erasure in Diffusion Models: A Theoretical Perspective on Security and Robustness](https://mlsanity.com/paper/2509.12024/) (2025) - [SAVTrack: Selective Vote Aggregation for Reliability-Aware Point Cloud Tracking](https://mlsanity.com/paper/2609.16662/) (2026) - [MAETrack: Unleashing the Potential of Pretrained Geometric Priors for 3D Single Object Tracking](https://mlsanity.com/paper/2609.16695/) (2026) ## Abstract (arXiv) 3D single object tracking is essential in autonomous driving and robotics. Existing methods often struggle with sparse and incomplete point cloud scenarios. To address these limitations, we propose a Multimodal-guided Virtual Cues Projection (MVCP) scheme that generates virtual cues to enrich sparse point clouds. Additionally, we introduce an enhanced tracker MVCTrack based on the generated virtual cues. Specifically, the MVCP scheme seamlessly integrates RGB sensors into LiDAR-based systems, leveraging a set of 2D detections to create dense 3D virtual cues that significantly improve the sparsity of point clouds. These virtual cues can naturally integrate with existing LiDAR-based 3D trackers, yielding substantial performance gains. Extensive experiments demonstrate that our method achieves competitive performance on the NuScenes dataset.