# OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering

arXiv: https://arxiv.org/abs/2503.12968 · HTML: https://mlsanity.com/paper/2503.12968/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 86 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 30 of 48)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://mlsanity.com/paper/2003.01251/) (2020)
- [ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries](https://mlsanity.com/paper/2208.01582/) (2022)
- [GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning](https://mlsanity.com/paper/2006.07327/) (2020)
- [SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking](https://mlsanity.com/paper/2111.09621/) (2021)
- [MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries](https://mlsanity.com/paper/2205.00613/) (2022)
- [EagerMOT: 3D Multi-Object Tracking via Sensor Fusion](https://mlsanity.com/paper/2104.14682/) (2021)
- [Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2012.13755/) (2020)
- [LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs](https://mlsanity.com/paper/2206.10555/) (2022)
- [Exploring Simple 3D Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2108.10312/) (2021)
- [Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking](https://mlsanity.com/paper/2302.03802/) (2023)
- [CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion](https://mlsanity.com/paper/2209.02540/) (2022)
- [JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset](https://mlsanity.com/paper/2002.08397/) (2020)
- [Learnable Online Graph Representations for 3D Multi-Object Tracking](https://mlsanity.com/paper/2104.11747/) (2021)
- [DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association](https://mlsanity.com/paper/2202.12100/) (2022)
- [Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering](https://mlsanity.com/paper/1802.09975/) (2018)
- [Poly-MOT: A Polyhedral Framework For 3D Multi-Object Tracking](https://mlsanity.com/paper/2307.16675/) (2023)
- [Poisson multi-Bernoulli mixture filter: direct derivation and implementation](https://mlsanity.com/paper/1703.04264/) (2017)
- [PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?](https://mlsanity.com/paper/2208.01957/) (2022)
- [Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving](https://mlsanity.com/paper/2108.04602/) (2021)
- [ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking](https://mlsanity.com/paper/2211.03919/) (2022)
- [3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking](https://mlsanity.com/paper/2308.06635/) (2023)
- [RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud](https://mlsanity.com/paper/2309.09737/) (2023)

## Built on by (most cited first, 1 of 1)

- [Seg2Track++: Probabilistic Track Validation and Data Association for Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/2606.03875/) (2026)

## Abstract (arXiv)

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving.