Facade Reconstruction with Generalized 2.5d Grids
Reconstructing fine facade geometry from MMS lidar data remains a challenge: In addition to being inherently sparse, the point cloud provided by a single street point of view is necessarily incomplete. We propose a simple framework to estimate the facade surface with a deformable 2.5d grid. Computations are performed in a "sensor-oriented" coordinate system that maximizes consistency with the data. the algorithm allows to retrieve the facade geometry without priori knowledge. It can thus be automatically applied to a large amount of data in spite of the variability of encountered architectural forms. The 2.5d image structure of the output makes it compatible with storage and real-time constraints of immersive navigation.