# labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds

arXiv: https://arxiv.org/abs/2103.04970 · HTML: https://mlsanity.com/paper/2103.04970/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams](https://mlsanity.com/paper/1905.00525/) (2019)
- [LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking](https://mlsanity.com/paper/1904.09085/) (2019)

## Built on by (most cited first, 8 of 8)

- [TUMTraf V2X Cooperative Perception Dataset](https://mlsanity.com/paper/2403.01316/) (2024)
- [A survey of image labelling for computer vision applications](https://mlsanity.com/paper/2104.08885/) (2021)
- [ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learning](https://mlsanity.com/paper/2303.06250/) (2023)
- [Survey and Systematization of 3D Object Detection Models and Methods](https://mlsanity.com/paper/2201.09354/) (2022)
- [RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving](https://mlsanity.com/paper/2303.05203/) (2023)
- [Object Pose Estimation using Mid-level Visual Representations](https://mlsanity.com/paper/2203.01449/) (2022)
- [Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry](https://mlsanity.com/paper/2110.09023/) (2021)
- [RecolorCloud: A Point Cloud Tool for Recoloring, Segmentation, and Conversion](https://mlsanity.com/paper/2310.12470/) (2023)

## Abstract (arXiv)

Within the past decade, the rise of applications based on artificial intelligence (AI) in general and machine learning (ML) in specific has led to many significant contributions within different domains. The applications range from robotics over medical diagnoses up to autonomous driving. However, nearly all applications rely on trained data. In case this data consists of 3D images, it is of utmost importance that the labeling is as accurate as possible to ensure high-quality outcomes of the ML models. Labeling in the 3D space is mostly manual work performed by expert workers, where they draw 3D bounding boxes around target objects the ML model should later automatically identify, e.g., pedestrians for autonomous driving or cancer cells within radiography. While a small range of recent 3D labeling tools exist, they all share three major shortcomings: (i) they are specified for autonomous driving applications, (ii) they lack convenience and comfort functions, and (iii) they have high dependencies and little flexibility in data format. Therefore, we propose a novel labeling tool for 3D object detection in point clouds to address these shortcomings.