# PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds

arXiv: https://arxiv.org/abs/2002.11559 · HTML: https://mlsanity.com/paper/2002.11559/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

## Built on by (most cited first, 20 of 20)

- [Ground-aware Monocular 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2102.00690/) (2021)
- [3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds](https://mlsanity.com/paper/2111.04426/) (2021)
- [3D-SiamRPN: An End-to-End Learning Method for Real-Time 3D Single Object Tracking Using Raw Point Cloud](https://mlsanity.com/paper/2108.05630/) (2021)
- [What Matters for 3D Scene Flow Network](https://mlsanity.com/paper/2207.09143/) (2022)
- [FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection](https://mlsanity.com/paper/2105.07647/) (2021)
- [Real-time 3D Single Object Tracking with Transformer](https://mlsanity.com/paper/2209.00860/) (2022)
- [MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error Propagation](https://mlsanity.com/paper/2110.09481/) (2021)
- [DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds](https://mlsanity.com/paper/2308.04383/) (2023)
- [PG-RCNN: Semantic Surface Point Generation for 3D Object Detection](https://mlsanity.com/paper/2307.12637/) (2023)
- [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://mlsanity.com/paper/2205.10766/) (2022)
- [3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion](https://mlsanity.com/paper/2209.13130/) (2022)
- [Sequential Point Clouds: A Survey](https://mlsanity.com/paper/2204.09337/) (2022)
- [DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation](https://mlsanity.com/paper/2203.02157/) (2022)
- [Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance](https://mlsanity.com/paper/2209.04945/) (2022)
- [Sports Analysis and VR Viewing System Based on Player Tracking and Pose Estimation with Multimodal and Multiview Sensors](https://mlsanity.com/paper/2405.01112/) (2024)
- [LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking](https://mlsanity.com/paper/2505.12753/) (2025)
- [Hierarchical Adaptive Voxel-guided Sampling for Real-time Applications in Large-scale Point Clouds](https://mlsanity.com/paper/2305.14306/) (2023)
- [PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection](https://mlsanity.com/paper/2408.14600/) (2024)
- [Enabling the Evaluation of Driver Physiology Via Vehicle Dynamics](https://mlsanity.com/paper/2309.04078/) (2023)
- [TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction](https://mlsanity.com/paper/2403.11695/) (2024)

## Abstract (arXiv)

Recent machine learning-based multi-object tracking (MOT) frameworks are becoming popular for 3-D point clouds. Most traditional tracking approaches use filters (e.g., Kalman filter or particle filter) to predict object locations in a time sequence, however, they are vulnerable to extreme motion conditions, such as sudden braking and turning. In this letter, we propose PointTrackNet, an end-to-end 3-D object detection and tracking network, to generate foreground masks, 3-D bounding boxes, and point-wise tracking association displacements for each detected object. The network merely takes as input two adjacent point-cloud frames. Experimental results on the KITTI tracking dataset show competitive results over the state-of-the-arts, especially in the irregularly and rapidly changing scenarios.