Sudipta N. Sinha, Drew Steedly, and Richard Szeliski
29 September 2009
We present a novel multi-view stereo method designed for image-based rendering that generates piecewise planar depth maps from an unordered collection of photographs.
First a discrete set of 3D plane candidates are computed based on a sparse point cloud of the scene (recovered by structure from motion) and sparse 3D line segments reconstructed from multiple views. Next, evidence is accumulated for each plane using 3D point and line incidence and photo-consistency cues. Finally, a piecewise planar depth map is recovered for each image by solving a multi-label Markov Random Field (MRF) optimization problem using graph-cuts. Our novel energy minimization formulation exploits high-level scene information. It incorporates geometric constraints derived from vanishing directions, enforces free space violation constraints based on ray visibility of 3D points and 3D lines and imposes smoothness priors specific to planes that intersect.
We demonstrate the effectiveness of our approach on a wide variety of outdoor and indoor datasets. The view interpolation results are perceptually pleasing, as straight lines are preserved and holes are minimized even for challenging scenes with non-Lambertian and textureless surfaces.
In Twelfth IEEE International Conference on Computer Vision (ICCV 2009)
© 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. http://www.ieee.org/