# Score refinement for confidence-based 3D multi-object tracking

arXiv: https://arxiv.org/abs/2107.04327 · HTML: https://mlsanity.com/paper/2107.04327/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 31 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 23 of 23)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [PointPainting: Sequential Fusion for 3D Object Detection](https://mlsanity.com/paper/1911.10150/) (2019)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://mlsanity.com/paper/1902.06326/) (2019)
- [Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection](https://mlsanity.com/paper/1908.09492/) (2019)
- [End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds](https://mlsanity.com/paper/1910.06528/) (2019)
- [GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning](https://mlsanity.com/paper/2006.07327/) (2020)
- [What You See is What You Get: Exploiting Visibility for 3D Object Detection](https://mlsanity.com/paper/1912.04986/) (2019)
- [Probabilistic 3D Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2001.05673/) (2020)
- [3D Multi-Object Tracking: A Baseline and New Evaluation Metrics](https://mlsanity.com/paper/1907.03961/) (2019)
- [Deep Network Flow for Multi-Object Tracking](https://mlsanity.com/paper/1706.08482/) (2017)
- [Improving 3D Object Detection through Progressive Population Based Augmentation](https://mlsanity.com/paper/2004.00831/) (2020)
- [FANTrack: 3D Multi-Object Tracking with Feature Association Network](https://mlsanity.com/paper/1905.02843/) (2019)

## Built on by (most cited first, 30 of 32)

- [VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking](https://mlsanity.com/paper/2303.11301/) (2023)
- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [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)
- [FocalFormer3D : Focusing on Hard Instance for 3D Object Detection](https://mlsanity.com/paper/2308.04556/) (2023)
- [CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion](https://mlsanity.com/paper/2209.02540/) (2022)
- [Immortal Tracker: Tracklet Never Dies](https://mlsanity.com/paper/2111.13672/) (2021)
- [TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM](https://mlsanity.com/paper/2210.16204/) (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)
- [Learning Cooperative Trajectory Representations for Motion Forecasting](https://mlsanity.com/paper/2311.00371/) (2023)
- [ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2303.15334/) (2023)
- [GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles](https://mlsanity.com/paper/2206.10255/) (2022)
- [MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error Propagation](https://mlsanity.com/paper/2110.09481/) (2021)
- [TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses](https://mlsanity.com/paper/2306.05888/) (2023)
- [Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Driving](https://mlsanity.com/paper/2502.16589/) (2025)
- [Fast-Poly: A Fast Polyhedral Framework For 3D Multi-Object Tracking](https://mlsanity.com/paper/2403.13443/) (2024)
- [LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds](https://mlsanity.com/paper/2308.09908/) (2023)
- [Conformal Trajectory Prediction with Multi-View Data Integration in Cooperative Driving](https://mlsanity.com/paper/2408.00374/) (2024)
- [RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework](https://mlsanity.com/paper/2409.11749/) (2024)
- [Open3DTrack: Towards Open-Vocabulary 3D Multi-Object Tracking](https://mlsanity.com/paper/2410.01678/) (2024)
- [IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter](https://mlsanity.com/paper/2502.09672/) (2025)
- [Radar Tracker: Moving Instance Tracking in Sparse and Noisy Radar Point Clouds](https://mlsanity.com/paper/2507.03441/) (2025)
- [Offline-Poly: A Polyhedral Framework For Offline 3D Multi-Object Tracking](https://mlsanity.com/paper/2602.13772/) (2026)
- [Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar](https://mlsanity.com/paper/2502.01357/) (2025)
- [Stable at Any Speed: Speed-Driven Multi-Object Tracking with Learnable Kalman Filtering](https://mlsanity.com/paper/2508.00358/) (2025)
- [Fusion-Poly: A Polyhedral Framework Based on Spatial-Temporal Fusion for 3D Multi-Object Tracking](https://mlsanity.com/paper/2603.08199/) (2026)
- [RegTrack: Simplicity Beneath Complexity in Robust Multi-Modal 3D Multi-Object Tracking](https://mlsanity.com/paper/2409.00618/) (2024)
- [Delving into Dynamic Scene Cue-Consistency for Robust 3D Multi-Object Tracking](https://mlsanity.com/paper/2508.11323/) (2025)
- [End-to-End 3-D Spatiotemporal Perception with Multimodal Fusion and V2X Collaboration](https://mlsanity.com/paper/2512.21831/) (2025)

## Abstract (arXiv)

Multi-object tracking is a critical component in autonomous navigation, as it provides valuable information for decision-making. Many researchers tackled the 3D multi-object tracking task by filtering out the frame-by-frame 3D detections; however, their focus was mainly on finding useful features or proper matching metrics. Our work focuses on a neglected part of the tracking system: score refinement and tracklet termination. We show that manipulating the scores depending on time consistency while terminating the tracklets depending on the tracklet score improves tracking results. We do this by increasing the matched tracklets' score with score update functions and decreasing the unmatched tracklets' score. Compared to count-based methods, our method consistently produces better AMOTA and MOTA scores when utilizing various detectors and filtering algorithms on different datasets. The improvements in AMOTA score went up to 1.83 and 2.96 in MOTA. We also used our method as a late-fusion ensembling method, and it performed better than voting-based ensemble methods by a solid margin. It achieved an AMOTA score of 67.6 on nuScenes test evaluation, which is comparable to other state-of-the-art trackers. Code is publicly available at: urlhttps://github.com/cogsys-tuebingen/CBMOT.