Multi-view stereo via depth map fusion: A coordinate decent optimization method

Multi-view stereo (MVS) plays a critical role in many practically important vision applications. Among the existing MVS methods, one typical approach is to fuse the depth maps from different views via minimization of the energy functional. However, these methods usually have expensive computational cost and are inflexible for extending to large neighborhood, leading to long run time and reconstruction artifacts. In this work, we propose a simple, efficient and flexible depth-map-fusion-based MVS reconstruction method: CoD-Fusion. The core idea of the method is to minimize the anisotropic or isotropic TV+L₁energy functional using the coordinate decent (CoD) algorithm. CoD performs TV+L₁minimization via solving a serial of voxel-wise L₁minimization sub-problems which can be efficiently solved using fast weighted median filtering (WMF). We then extend WMF to larger neighborhood to suppress reconstruction artifacts. The results of quantitative and qualitative evaluation validate the flexibility and efficiency of CoD-Fusion as a promising method for large scale MVS reconstruction.