Abstract: In this thesis, we address the problem of 3D reconstruction from a sequence of calibrated street-level photographs with a simultaneous focus on scalability and the use of structure priors in Multi-View Stereo (MVS).
While both aspects have been studied broadly, existing scalable MVS approaches do not handle well the ubiquitous structural regularities, yet simple, of man-made environments. On the other hand, structure-aware 3D reconstruction methods are slow and scale poorly with the size of the input sequences and/or may even require additional restrictive information. The goal of this thesis is to reconcile scalability and structureawareness within common MVS grounds using soft, generic priors which encourage: (i) piecewise planarity, (ii) alignment of objects boundaries with image gradients and (iii) with vanishing directions (VDs), and (iv) objects co-planarity. To do so, we present the novel “Patchwork Stereo” framework which integrates photometric stereo from a handful of wide-baseline views and a sparse 3D point cloud combining robust 3D plane extraction and top-down image partitioning from a unified 2D-3D analysis in a principled Markov Random Field energy minimization.
We evaluate our contributions quantitatively and qualitatively on challenging urban datasets and illustrate results which are at least on par with state-of-the-art methods in terms of geometric structure, but achieved in several orders of magnitude faster paving the way for photo-realistic city-scale modeling.
Keywords: Multi-View Stereo, Structure Priors, 3D Reconstruction, Image-Based Modeling, Scalability, Top-Down Image Segmentation, Urban Modeling.
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