Crackstructures and Crackensembles: The Power of Multi-View for 2.5D Crack Detection

While research on structural crack segmentation at the image level remains highly active, progress beyond two dimensions has been limited. This stagnation is largely due to the lack of available data for crack detection in higher dimensions. To address this limitation, we introduce Crack-structures, a dataset tailored for real-world 2.5D crack segmentation, encompassing 15 segments from five distinct structures. Additionally, we present Crackensembles, a complementary semi-synthetic dataset that combines real textures with synthetic geometry to enhance the development of learning-based algorithms. Coupled with a baseline for multi-view crack instance segmentation, this work establishes a solid foundation for advancing algorithms that support real-world structural inspection.