# Learning Moving-Object Tracking with FMCW LiDAR

arXiv: https://arxiv.org/abs/2203.00959 · HTML: https://mlsanity.com/paper/2203.00959/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 18 of 18)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019)
- [Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2012.13755/) (2020)
- [4D Panoptic LiDAR Segmentation](https://mlsanity.com/paper/2102.12472/) (2021)
- [Exploring Simple 3D Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2108.10312/) (2021)
- [MOPT: Multi-Object Panoptic Tracking](https://mlsanity.com/paper/2004.08189/) (2020)
- [DICP: Doppler Iterative Closest Point Algorithm](https://mlsanity.com/paper/2201.11944/) (2022)
- [Automatic labelling of urban point clouds using data fusion](https://mlsanity.com/paper/2108.13757/) (2021)
- [Doppler velocity-based algorithm for Clustering and Velocity Estimation of moving objects](https://mlsanity.com/paper/2112.12984/) (2021)

## Built on by (most cited first, 5 of 5)

- [SparseDM: Toward Sparse Efficient Diffusion Models](https://mlsanity.com/paper/2404.10445/) (2024)
- [FMCW-LIO: A Doppler LiDAR-Inertial Odometry](https://mlsanity.com/paper/2609.29374/) (2026)
- [POD: Predictive Object Detection with Single-Frame FMCW LiDAR Point Cloud](https://mlsanity.com/paper/2504.05649/) (2025)
- [AevaScenes: An FMCW LiDAR Dataset and Benchmark for Long-Range Perception](https://mlsanity.com/paper/2609.33230/) (2026)
- [DiFF: Doppler-informed Flow Matching for Human Motion Flow](https://mlsanity.com/paper/2609.39098/) (2026)

## Abstract (arXiv)

In this paper, we propose a learning-based moving-object tracking method utilizing our newly developed LiDAR sensor, Frequency Modulated Continuous Wave (FMCW) LiDAR. Compared with most existing commercial LiDAR sensors, our FMCW LiDAR can provide additional Doppler velocity information to each 3D point of the point clouds. Benefiting from this, we can generate instance labels as ground truth in a semi-automatic manner. Given the labels, we propose a contrastive learning framework, which pulls together the features from the same instance in embedding space and pushes apart the features from different instances, to improve the tracking quality. Extensive experiments are conducted on our recorded driving data, and the results show that our method outperforms the baseline methods by a large margin.