Master Thesis Tree Detection and Reconstruction using Multi-Sensor Fusion

Digital twins and virtual replicas of physical entities, processes, or systems have a broad range of applications across various industries and domains. Their unique capacity to connect the physical and digital worlds empowers organizations to simulate, monitor, and optimize real-world assets and operations, making them a promising technological advancement. However, generating functional replicas of indoor or outdoor environments typically involves a substantial amount of manual effort, especially when striving for a high level of detail. In the context of outdoor models, the reconstruction and localization of lifelike vegetation, such as trees, can be particularly time-consuming. To streamline the generation process, leveraging deep learning techniques and effectively harnessing diverse sensor data, can offer substantial benefits.

Therefore, the objective of this work is the detection and reconstruction of trees employing data from multiple optical sensing technologies. This includes