# Automatic Labeling to Generate Training Data for Online LiDAR-based Moving Object Segmentation

arXiv: https://arxiv.org/abs/2201.04501 · HTML: https://mlsanity.com/paper/2201.04501/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2022.

## Built on by (most cited first, 27 of 27)

- [Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications](https://mlsanity.com/paper/2311.17663/) (2023)
- [Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions](https://mlsanity.com/paper/2206.04129/) (2022)
- [Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation](https://mlsanity.com/paper/2207.02201/) (2022)
- [MF-MOS: A Motion-Focused Model for Moving Object Segmentation](https://mlsanity.com/paper/2401.17023/) (2024)
- [InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data](https://mlsanity.com/paper/2303.03909/) (2023)
- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
- [Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments](https://mlsanity.com/paper/2208.07364/) (2022)
- [MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation with Bird's Eye View based Appearance and Motion Features](https://mlsanity.com/paper/2305.07336/) (2023)
- [Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series](https://mlsanity.com/paper/2212.14750/) (2022)
- [A Dynamic Points Removal Benchmark in Point Cloud Maps](https://mlsanity.com/paper/2307.07260/) (2023)
- [3D LiDAR Mapping in Dynamic Environments Using a 4D Implicit Neural Representation](https://mlsanity.com/paper/2405.03388/) (2024)
- [LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds](https://mlsanity.com/paper/2311.01444/) (2023)
- [T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds](https://mlsanity.com/paper/2309.08302/) (2023)
- [SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud](https://mlsanity.com/paper/2406.16279/) (2024)
- [HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors](https://mlsanity.com/paper/2408.06328/) (2024)
- [Teachers in concordance for pseudo-labeling of 3D sequential data](https://mlsanity.com/paper/2207.06079/) (2022)
- [Survey on Datasets for Perception in Unstructured Outdoor Environments](https://mlsanity.com/paper/2404.18750/) (2024)
- [XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation](https://mlsanity.com/paper/2310.08182/) (2023)
- [LiFlow: Flow Matching for 3D LiDAR Scene Completion](https://mlsanity.com/paper/2602.02232/) (2026)
- [Aligning Bird-Eye View Representation of Point Cloud Sequences using Scene Flow](https://mlsanity.com/paper/2305.02909/) (2023)
- [CV-MOS: A Cross-View Model for Motion Segmentation](https://mlsanity.com/paper/2408.13790/) (2024)
- [Near-Shore Mapping for Detection and Tracking of Vessels](https://mlsanity.com/paper/2502.18368/) (2025)
- [AutoVDC: Automated Vision Data Cleaning Using Vision-Language Models](https://mlsanity.com/paper/2507.12414/) (2025)
- [A Spatiotemporal Correspondence Approach to Unsupervised LiDAR Segmentation with Traffic Applications](https://mlsanity.com/paper/2308.12433/) (2023)
- [A Multi-model Approach for Video Data Retrieval in Autonomous Vehicle Development](https://mlsanity.com/paper/2410.03580/) (2024)
- [Temporal Overlapping Prediction: A Self-supervised Pre-training Method for LiDAR Moving Object Segmentation](https://mlsanity.com/paper/2503.07167/) (2025)
- [KDMOS:Knowledge Distillation for Motion Segmentation](https://mlsanity.com/paper/2506.14130/) (2025)

## Abstract (arXiv)

Understanding the scene is key for autonomously navigating vehicles and the ability to segment the surroundings online into moving and non-moving objects is a central ingredient for this task. Often, deep learning-based methods are used to perform moving object segmentation (MOS). The performance of these networks, however, strongly depends on the diversity and amount of labeled training data, information that may be costly to obtain. In this paper, we propose an automatic data labeling pipeline for 3D LiDAR data to save the extensive manual labeling effort and to improve the performance of existing learning-based MOS systems by automatically generating labeled training data. Our proposed approach achieves this by processing the data offline in batches. It first exploits an occupancy-based dynamic object removal to detect possible dynamic objects coarsely. Second, it extracts segments among the proposals and tracks them using a Kalman filter. Based on the tracked trajectories, it labels the actually moving objects such as driving cars and pedestrians as moving. In contrast, the non-moving objects, e.g., parked cars, lamps, roads, or buildings, are labeled as static. We show that this approach allows us to label LiDAR data highly effectively and compare our results to those of other label generation methods. We also train a deep neural network with our auto-generated labels and achieve similar performance compared to the one trained with manual labels on the same data, and an even better performance when using additional datasets with labels generated by our approach. Furthermore, we evaluate our method on multiple datasets using different sensors and our experiments indicate that our method can generate labels in diverse environments.