Energy-based multi-view piecewise planar stereo

Abstract

The piecewise planar model (PPM) is an effective means of approximating a complex scene by using planar patches to give a complete interpretation of the spatial points reconstructed from projected 2D images. The traditional piecewise planar stereo methods suffer from either a very restricted number of directions for plane detection or heavy reliance on the segmentation accuracy of superpixels. To address these issues, we propose a new multi-view piecewise planar stereo method in this paper. Our method formulates the problem of complete scene reconstruction as a multi-level energy minimization problem. To detect planes along principal directions, a novel energy formulation with pair-wise potentials is used to assign an optimal plane for each superpixel in an iterative manner, where reliable scene priors and geometric constraints are incorporated to enhance the modeling efficacy and inference efficiency. To detect non-principal-direction planes, we adopt a multi-direction plane sweeping with a restricted search space method to generate reliable candidate planes. To handle the multi-surface straddling problem of a single superpixel, a superpixel sub-segmenting scheme is proposed and a robust Pn Potts model-like higher-order potential is introduced to refine the resulting depth map. Our method is a natural integration of pixel- and superpixel-level multi-view stereos under a unified energy minimization framework. Experimental results for standard data sets and our own data sets show that our proposed method can satisfactorily handle many challenging factors (e.g., slanted surfaces and poorly textured regions) and can obtain accurate piecewise planar depth maps.