# AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics

arXiv: https://arxiv.org/abs/2008.08063 · HTML: https://mlsanity.com/paper/2008.08063/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

## Built on by (most cited first, 30 of 63)

- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [End-to-End Autonomous Driving through V2X Cooperation](https://mlsanity.com/paper/2404.00717/) (2024)
- [4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation](https://mlsanity.com/paper/2209.14858/) (2022)
- [ICP-Flow: LiDAR Scene Flow Estimation with ICP](https://mlsanity.com/paper/2402.17351/) (2024)
- [TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM](https://mlsanity.com/paper/2210.16204/) (2022)
- [Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles](https://mlsanity.com/paper/2204.09151/) (2022)
- [ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2303.15334/) (2023)
- [MotionTrack: End-to-End Transformer-based Multi-Object Tracing with LiDAR-Camera Fusion](https://mlsanity.com/paper/2306.17000/) (2023)
- [HoloVIC: Large-scale Dataset and Benchmark for Multi-Sensor Holographic Intersection and Vehicle-Infrastructure Cooperative](https://mlsanity.com/paper/2403.02640/) (2024)
- [Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor Scenes](https://mlsanity.com/paper/2308.00628/) (2023)
- [SpOT: Spatiotemporal Modeling for 3D Object Tracking](https://mlsanity.com/paper/2207.05856/) (2022)
- [Minkowski Tracker: A Sparse Spatio-Temporal R-CNN for Joint Object Detection and Tracking](https://mlsanity.com/paper/2208.10056/) (2022)
- [RoboSense: Large-scale Dataset and Benchmark for Egocentric Robot Perception and Navigation in Crowded and Unstructured Environments](https://mlsanity.com/paper/2408.15503/) (2024)
- [3D Video Object Detection with Learnable Object-Centric Global Optimization](https://mlsanity.com/paper/2303.15416/) (2023)
- [Track, Check, Repeat: An EM Approach to Unsupervised Tracking](https://mlsanity.com/paper/2104.03424/) (2021)
- [Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detection](https://mlsanity.com/paper/2307.01462/) (2023)
- [PD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues](https://mlsanity.com/paper/2501.11288/) (2025)
- [PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior](https://mlsanity.com/paper/2503.06477/) (2025)
- [DriveE2E: Closed-Loop Benchmark for End-to-End Autonomous Driving through Real-to-Simulation](https://mlsanity.com/paper/2509.23922/) (2025)
- [Vision meets mmWave Radar: 3D Object Perception Benchmark for Autonomous Driving](https://mlsanity.com/paper/2311.10261/) (2023)
- [A two-stage data association approach for 3D Multi-object Tracking](https://mlsanity.com/paper/2101.08684/) (2021)
- [Urban Traffic Surveillance (UTS): A fully probabilistic 3D tracking approach based on 2D detections](https://mlsanity.com/paper/2105.14993/) (2021)
- [Lidar with Velocity: Correcting Moving Objects Point Cloud Distortion from Oscillating Scanning Lidars by Fusion with Camera](https://mlsanity.com/paper/2111.09497/) (2021)
- [SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and Tracking](https://mlsanity.com/paper/2206.14451/) (2022)
- [StreamMOTP: Streaming and Unified Framework for Joint 3D Multi-Object Tracking and Trajectory Prediction](https://mlsanity.com/paper/2406.19844/) (2024)
- [GSLAMOT: A Tracklet and Query Graph-based Simultaneous Locating, Mapping, and Multiple Object Tracking System](https://mlsanity.com/paper/2408.09191/) (2024)
- [Enhancing 3D Semantic Scene Completion with a Refinement Module](https://mlsanity.com/paper/2512.18363/) (2025)
- [TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching](https://mlsanity.com/paper/2302.02157/) (2023)
- [Tracking Objects with 3D Representation from Videos](https://mlsanity.com/paper/2306.05416/) (2023)
- [Fast and Modular Autonomy Software for Autonomous Racing Vehicles](https://mlsanity.com/paper/2408.15425/) (2024)

## Abstract (arXiv)

3D multi-object tracking (MOT) is essential to applications such as autonomous driving. Recent work focuses on developing accurate systems giving less attention to computational cost and system complexity. In contrast, this work proposes a simple real-time 3D MOT system with strong performance. Our system first obtains 3D detections from a LiDAR point cloud. Then, a straightforward combination of a 3D Kalman filter and the Hungarian algorithm is used for state estimation and data association. Additionally, 3D MOT datasets such as KITTI evaluate MOT methods in 2D space and standardized 3D MOT evaluation tools are missing for a fair comparison of 3D MOT methods. We propose a new 3D MOT evaluation tool along with three new metrics to comprehensively evaluate 3D MOT methods. We show that, our proposed method achieves strong 3D MOT performance on KITTI and runs at a rate of 207.4 FPS on the KITTI dataset, achieving the fastest speed among modern 3D MOT systems. Our code is publicly available at http://www.xinshuoweng.com/projects/AB3DMOT.