High-quality 3D mesh models are increasingly used in urban applications ranging from planning and simulation to environmental monitoring. While aerial imagery provides a practical balance between resolution and coverage, conventional Structure from Motion (SfM) and Multi-View Stereo (MVS) pipelines apply uniform processing to all image regions, often overlooking their geometric relevance. This research investigates whether reconstruction efficiency can be improved by selectively focusing dense matching efforts on image regions most critical to mesh quality. We explore the use of the Segment Anything Model (SAM) to guide dense matching in oblique aerial imagery, applying it for building-level segmentation and importance estimation via entropy and edge-based scoring. Initially, we tested SAM for sparse reconstruction guidance but encountered poor performance. We then shifted to evaluating whether SAMderived importance maps could enable region-aware thresholding to improve mesh reconstruction. These methods were benchmarked against a simpler Canny edge-based distance approach across varying thresholds and scenes. Results show that SAM-based methods can reduce memory usage while maintaining mesh quality at low to moderate thresholds (up to 0.4). However, contrary to expectations, Canny edge detection consistently outperformed SAM across most quality and efficiency metrics, offering better spatial coverage, lower computational overhead, and more stable performance. While SAM-based thresholding led to file size reductions of up to 16% and marginal runtime gains during dense matching ( 2.3%), these were offset by the costly preprocessing pipeline required to generate segmentation masks and importance maps. Overall, this thesis contributes an evaluation pipeline for image region-specific reconstruction strategies and highlights that while SAM shows potential for memory-efficient modeling, simpler methods like Canny edge detection may offer better trade-offs for scalable, time-sensitive workflows. Future research should focus on faster importance estimation techniques and pipeline adaptation for more complex, city-scale datasets.

This report documents the development of Delft3D, a web-based 3D application, designed to visualise Delft’s urban features—such as buildings, vegetation, and water bodies—using open datasets. Developed for the Municipality of Delft, Delft3D customises the Netherlands3D platform to provide accessible, interactive visualisation for residents and employees of the municipality. This report primarily focuses on the process of development and only explains the implemented features conceptually. After the explanation, we provide a concise reflection on the hurdles we faced during the project and we give an overview of the limiting factors.