# BiTrack: Bidirectional Offline 3D Multi-Object Tracking Using Camera-LiDAR Data

arXiv: https://arxiv.org/abs/2406.18414 · HTML: https://mlsanity.com/paper/2406.18414/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Frustum PointNets for 3D Object Detection from RGB-D Data](https://mlsanity.com/paper/1711.08488/) (2017)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022)
- [CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection](https://mlsanity.com/paper/2009.00784/) (2020)
- [Offboard 3D Object Detection from Point Cloud Sequences](https://mlsanity.com/paper/2103.05073/) (2021)
- [Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth](https://mlsanity.com/paper/1906.11109/) (2019)
- [Virtual Sparse Convolution for Multimodal 3D Object Detection](https://mlsanity.com/paper/2303.02314/) (2023)
- [JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset](https://mlsanity.com/paper/2002.08397/) (2020)
- [Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving](https://mlsanity.com/paper/2108.04602/) (2021)
- [Object Permanence Emerges in a Random Walk along Memory](https://mlsanity.com/paper/2204.01784/) (2022)

## Built on by (most cited first, 6 of 6)

- [MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving](https://mlsanity.com/paper/2409.16149/) (2024)
- [HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking](https://mlsanity.com/paper/2501.01275/) (2025)
- [LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking](https://mlsanity.com/paper/2505.12753/) (2025)
- [Offline-Poly: A Polyhedral Framework For Offline 3D Multi-Object Tracking](https://mlsanity.com/paper/2602.13772/) (2026)
- [FutrTrack: A Camera-LiDAR Fusion Transformer for 3D Multiple Object Tracking](https://mlsanity.com/paper/2510.19981/) (2025)
- [MotionSync: Non-Causal Refinement of Causal Tracker for Label-Efficient 3D Perception](https://mlsanity.com/paper/2608.29567/) (2026)

## Abstract (arXiv)

Compared with real-time multi-object tracking (MOT), offline multi-object tracking (OMOT) has the advantages to perform 2D-3D detection fusion, erroneous link correction, and full track optimization but has to deal with the challenges from bounding box misalignment and track evaluation, editing, and refinement. This paper proposes "BiTrack", a 3D OMOT framework that includes modules of 2D-3D detection fusion, initial trajectory generation, and bidirectional trajectory re-optimization to achieve optimal tracking results from camera-LiDAR data. The novelty of this paper includes threefold: (1) development of a point-level object registration technique that employs a density-based similarity metric to achieve accurate fusion of 2D-3D detection results; (2) development of a set of data association and track management skills that utilizes a vertex-based similarity metric as well as false alarm rejection and track recovery mechanisms to generate reliable bidirectional object trajectories; (3) development of a trajectory re-optimization scheme that re-organizes track fragments of different fidelities in a greedy fashion, as well as refines each trajectory with completion and smoothing techniques. The experiment results on the KITTI dataset demonstrate that BiTrack achieves the state-of-the-art performance for 3D OMOT tasks in terms of accuracy and efficiency.