Labelling point clouds in VR

Recent advancements in Virtual Reality (VR) technology have extended its applications beyond entertainment, offering promising tools for professional fields such as 3D data annotation. This paper explores the use of VR for labelling 3D point clouds in forestry and cultural heritage datasets. We employ Labelling Flora, an open-source VR annotation tool, to re-label three existing cultural heritage and one forestry datasets and assess the effectiveness of VR-based annotations in training machine learning models. By comparing the accuracy, precision, and F1-score of inference models trained with VR-generated labels to those trained with traditional desktop labelling methods, we evaluate the potential of VR to streamline labour-intensive annotation tasks. Our results indicate that VR enables intuitive 3D segmentation, even for individuals without technical expertise, particularly for very complex scenes, improving labelling efficiency and contributing to the overall automation of complex datasets. This study highlights therefore the potential of VR to enhance other workflows and make complex tasks more accessible to domain experts who may not be familiar with 3D data thus refining data accuracy and reliability.