By combining terrestrial panorama images and aerial imagery, or using LiDAR, large 3D point clouds can be generated for 3D city modeling. We describe an algorithm for change detection in point clouds, including three new contributions: change detection for LOD2 models compared to
3D point clouds, the application of detected changes for creating extended and textured LOD2 models, and change detection between point clouds of different years. Overall, LOD2 model-to-point-cloud changes are reliably found in practice, and the algorithm achieves a precision of 0.955 and
recall of 0.983 on a synthetic dataset. Despite not having a watertight model, texturing results are visually promising, improving over directly textured LOD2 models.