Piecewise Planar Stereo for Image-based Rendering

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 ap...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Sudipta N. Sinha, Drew Steedly and Richard Szeliski
• Keywords: Computer Vision, ICCV 2009, Markov Random Fields (MRF), Plane-fitting, View Interpolation