This chapter proposes an automatic approach that generates 3D photo-realistic building models from images captured along the streets at ground level. We first develop a multi-view semantic segmentation method that recognizes and segments each image at pixel level into semantically meaningful areas, each labeled with a specific object class, such as buildings, sky, ground, vegetation and cars. A partition scheme is then introduced to separate buildings into independent blocks using the major line structures of the scene. Finally, for each block, we propose an inverse patch-based orthographic composition and structure analysis method for façade modeling that efficiently regularizes the noisy and missing reconstructed 3D data. Our system has the distinct advantage of producing visually compelling results by imposing strong priors of building regularity.We demonstrate the fully automatic system on a typical city example to validate our methodology.
KeywordsInput Image Texture Image City Modeling Label Image Block Partition
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