FEASIBILITY STUDY OF USING VIRTUAL REALITY FOR INTERACTIVE AND IMMERSIVE SEMANTIC SEGMENTATION OF SINGLE TREE STEMS
Keywords: Virtual Reality, Point Cloud Segmentation, Training Dataset Creation, Tree Stem Labelling, Microhabitats
Abstract. Forest digitisation is one of the next major challenges to be tackled in the forestry domain. As a consequence of tremendous advances in 3D scanning technologies, broad areas of forest can be mapped in 3D dramatically faster than 20 years ago. Consequently, capturing 3D forest point clouds with the use of 3D sensing technologies – such as lidar – is becoming predominant in the field of forestry. However, the processing of 3D point clouds to bring semantics to the 3D forestry data – e.g. by linking them with ecological values – has not seen similar advancements. Therefore, in this paper we consider a novel approach based on the use of VR (Virtual reality) as a potential solution for deriving biodiversity from 3D point clouds acquired in the field. That is, we developed a VR labelling application to visualise forest point clouds and to perform the segmentation of several biodiversity components on tree stems e.g., mosses, lichens and bark pockets. Furthermore, the VR segmented point cloud was analysed with standard accuracy and precision metrics. Namely, the proposed VR application managed to achieve an IoU (Intersection over Union) rate value of 98.74% for the segmentation of bark pockets and resp. 93.71% for the moss and lichen classes. These encouraging results reinforce the potential for the proposed VR labelling method for other purposes in the future, for example for AI (Artificial Intelligence) training dataset creation.