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

TL;DR

labelCloud is a versatile, user-friendly 3D labeling tool designed to improve accuracy and flexibility in annotating point cloud data for various AI applications, overcoming limitations of existing solutions.

Contribution

We introduce labelCloud, a lightweight, domain-independent 3D labeling tool that enhances convenience and flexibility compared to existing autonomous driving-focused solutions.

Findings

Addresses key shortcomings of existing tools

Supports multiple data formats and domains

Improves labeling efficiency and accuracy

Abstract

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.