labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds
This tool aims to improve labeling efficiency for researchers and practitioners across various domains like robotics and medical imaging, though it appears incremental as it builds on existing labeling approaches.
The authors tackled the problem of manual 3D labeling for machine learning by developing a lightweight, domain-independent tool for 3D object detection in point clouds, addressing shortcomings in existing tools such as domain specificity and lack of convenience.
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.