# LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking

arXiv: https://arxiv.org/abs/1904.09085 · HTML: https://mlsanity.com/paper/1904.09085/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

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

- [TUMTraf V2X Cooperative Perception Dataset](https://mlsanity.com/paper/2403.01316/) (2024)
- [Auto4D: Learning to Label 4D Objects from Sequential Point Clouds](https://mlsanity.com/paper/2101.06586/) (2021)
- [OpenAnnotate3D: Open-Vocabulary Auto-Labeling System for Multi-modal 3D Data](https://mlsanity.com/paper/2310.13398/) (2023)
- [360^circ from a Single Camera: A Few-Shot Approach for LiDAR Segmentation](https://mlsanity.com/paper/2309.06197/) (2023)
- [SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences](https://mlsanity.com/paper/2102.10820/) (2021)
- [labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/2103.04970/) (2021)
- [An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation](https://mlsanity.com/paper/2301.10732/) (2023)
- [Towards Class-agnostic Tracking Using Feature Decorrelation in Point Clouds](https://mlsanity.com/paper/2202.13524/) (2022)
- [ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learning](https://mlsanity.com/paper/2303.06250/) (2023)
- [Towards Robust Few-shot Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2309.11228/) (2023)
- [iDet3D: Towards Efficient Interactive Object Detection for LiDAR Point Clouds](https://mlsanity.com/paper/2312.15449/) (2023)
- [From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation](https://mlsanity.com/paper/2302.03114/) (2023)
- [PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds](https://mlsanity.com/paper/2304.08591/) (2023)
- [A survey on deep learning approaches for data integration in autonomous driving system](https://mlsanity.com/paper/2306.11740/) (2023)
- [Label-Efficient LiDAR Panoptic Segmentation](https://mlsanity.com/paper/2503.02372/) (2025)
- [High-Fidelity Digital Twins for Bridging the Sim2Real Gap in LiDAR-Based ITS Perception](https://mlsanity.com/paper/2509.02904/) (2025)
- [RangeSAM: On the Potential of Visual Foundation Models for Range-View represented LiDAR segmentation](https://mlsanity.com/paper/2509.15886/) (2025)
- [Viewpoint Recommendation for Point Cloud Labeling through Interaction Cost Modeling](https://mlsanity.com/paper/2602.10871/) (2026)
- [HAT-4D: Lifting Monocular Video for 4D Multi-Object Interactions via Human-Agent Collaboration](https://mlsanity.com/paper/2606.28215/) (2026)
- [MeshBind: Point-to-Mesh Binding for Non-Rigid Scene Flow Automatic Annotation](https://mlsanity.com/paper/2610.12895/) (2026)

## Abstract (arXiv)

LiDAR (Light Detection And Ranging) is an essential and widely adopted sensor for autonomous vehicles, particularly for those vehicles operating at higher levels (L4-L5) of autonomy. Recent work has demonstrated the promise of deep-learning approaches for LiDAR-based detection. However, deep-learning algorithms are extremely data hungry, requiring large amounts of labeled point-cloud data for training and evaluation. Annotating LiDAR point cloud data is challenging due to the following issues: 1) A LiDAR point cloud is usually sparse and has low resolution, making it difficult for human annotators to recognize objects. 2) Compared to annotation on 2D images, the operation of drawing 3D bounding boxes or even point-wise labels on LiDAR point clouds is more complex and time-consuming. 3) LiDAR data are usually collected in sequences, so consecutive frames are highly correlated, leading to repeated annotations. To tackle these challenges, we propose LATTE, an open-sourced annotation tool for LiDAR point clouds. LATTE features the following innovations: 1) Sensor fusion: We utilize image-based detection algorithms to automatically pre-label a calibrated image, and transfer the labels to the point cloud. 2) One-click annotation: Instead of drawing 3D bounding boxes or point-wise labels, we simplify the annotation to just one click on the target object, and automatically generate the bounding box for the target. 3) Tracking: we integrate tracking into sequence annotation such that we can transfer labels from one frame to subsequent ones and therefore significantly reduce repeated labeling. Experiments show the proposed features accelerate the annotation speed by 6.2x and significantly improve label quality with 23.6% and 2.2% higher instance-level precision and recall, and 2.0% higher bounding box IoU. LATTE is open-sourced at https://github.com/bernwang/latte.