# S.T.A.R.-Track: Latent Motion Models for End-to-End 3D Object Tracking with Adaptive Spatio-Temporal Appearance Representations

arXiv: https://arxiv.org/abs/2306.17602 · HTML: https://mlsanity.com/paper/2306.17602/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 53 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers](https://mlsanity.com/paper/2203.11496/) (2022)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020)
- [PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images](https://mlsanity.com/paper/2206.01256/) (2022)
- [Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection](https://mlsanity.com/paper/2303.11926/) (2023)
- [Quasi-Dense Similarity Learning for Multiple Object Tracking](https://mlsanity.com/paper/2006.06664/) (2020)
- [Unifying Voxel-based Representation with Transformer for 3D Object Detection](https://mlsanity.com/paper/2206.00630/) (2022)
- [Multiple Object Tracking: A Literature Review](https://mlsanity.com/paper/1409.7618/) (2014)
- [An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection](https://mlsanity.com/paper/1904.09730/) (2019)
- [DETRs with Hybrid Matching](https://mlsanity.com/paper/2207.13080/) (2022)
- [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019)
- [ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries](https://mlsanity.com/paper/2208.01582/) (2022)
- [Deep Learning in Video Multi-Object Tracking: A Survey](https://mlsanity.com/paper/1907.12740/) (2019)
- [SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking](https://mlsanity.com/paper/2111.09621/) (2021)

## Built on by (most cited first, 12 of 12)

- [ADA-Track++: End-to-End Multi-Camera 3D Multi-Object Tracking with Alternating Detection and Association](https://mlsanity.com/paper/2405.08909/) (2024)
- [DualAD: Disentangling the Dynamic and Static World for End-to-End Driving](https://mlsanity.com/paper/2406.06264/) (2024)
- [CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception](https://mlsanity.com/paper/2507.19239/) (2025)
- [TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking](https://mlsanity.com/paper/2504.03258/) (2025)
- [Inverse++: Vision-Centric 3D Semantic Occupancy Prediction Assisted with 3D Object Detection](https://mlsanity.com/paper/2504.04732/) (2025)
- [Rethinking the Spatio-Temporal Alignment of End-to-End 3D Perception](https://mlsanity.com/paper/2512.23635/) (2025)
- [SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive Learning for End-to-End Multi-Camera 3D Tracking](https://mlsanity.com/paper/2411.06780/) (2024)
- [Offline-Poly: A Polyhedral Framework For Offline 3D Multi-Object Tracking](https://mlsanity.com/paper/2602.13772/) (2026)
- [SCATR: Mitigating New Instance Suppression in LiDAR-based Tracking-by-Attention via Second Chance Assignment and Track Query Dropout](https://mlsanity.com/paper/2603.01485/) (2026)
- [To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Software](https://mlsanity.com/paper/2508.20892/) (2025)
- [Fusion-Poly: A Polyhedral Framework Based on Spatial-Temporal Fusion for 3D Multi-Object Tracking](https://mlsanity.com/paper/2603.08199/) (2026)
- [GraphBEV++: Multi-Modal Feature Alignment for Autonomous Driving](https://mlsanity.com/paper/2606.16354/) (2026)

## Abstract (arXiv)

Following the tracking-by-attention paradigm, this paper introduces an object-centric, transformer-based framework for tracking in 3D. Traditional model-based tracking approaches incorporate the geometric effect of object- and ego motion between frames with a geometric motion model. Inspired by this, we propose S.T.A.R.-Track, which uses a novel latent motion model (LMM) to additionally adjust object queries to account for changes in viewing direction and lighting conditions directly in the latent space, while still modeling the geometric motion explicitly. Combined with a novel learnable track embedding that aids in modeling the existence probability of tracks, this results in a generic tracking framework that can be integrated with any query-based detector. Extensive experiments on the nuScenes benchmark demonstrate the benefits of our approach, showing state-of-the-art performance for DETR3D-based trackers while drastically reducing the number of identity switches of tracks at the same time.