NeedLR: Streamlining Point Cloud Annotation for Enhanced Machine Learning Integration

Gunner Stone, Alireza Tavakkoli

Abstract: With the rising popularity of remote sensing across various scientific and engineering disciplines, the demand for efficient analysis of point cloud data has surged. However, the inherent complexity and volume of point cloud data pose considerable obstacles to human annotation efforts, which are an essential step within the machine learning pipeline for generating accurate training datasets. NeedLR emerges as a solution, offering a robust, user-friendly platform tailored for precise and streamlined point cloud annotation. By harnessing GPU-accelerated visualization, NeedLR facilitates interactive 3D manipulation of point clouds, granting users an intuitive means to dive into their data. Further optimized for efficient RAM usage and employing parallel computing strategies, NeedLR achieves great performance across varied computing environments. Its accessible interface broadens user engagement, rendering it a prime candidate for crowdsourced annotation initiatives and enhancing its utility in machine learning endeavors. This paper presents NeedLR, exploring its development, key features, and the user-centric philosophy that shapes its design. We highlight the potential for NeedLR’s role in enhancing current point cloud annotation techniques, merging operational efficiency with broad access to empower users across disciplines.