Multi-View Stereo using Cross-View Depth Map Completion and Row-Column Depth Refinement

Inferring the three-dimensional structure of an object or scene from multi-view images is a hard inverse problem. In this paper, we propose a depth map-based method to reconstruct a 3D model from multiple calibrated images. The proposed method uses global and local optimization in two stages to estimate highly accurate depth maps. The first stage employs Particle Swarm Optimization to explore the search-space extensively and estimate coarse depth maps from downscaled images. A cross-view depth map completion step ensures that good depth estimates are transferred to neighboring views. This helps in removing outliers and fill in missing depth values with reliable estimates. In the second stage, a local optimizer refines the coarse depths by processing each row/column of pixels iteratively to improve photo-consistency and smoothness among neighboring depths. This procedure utilizes distance maps to handle textured and homogeneous regions adaptively. Experimental results on the Middlebury multi-view stereo benchmark demonstrate the effectiveness of our method in producing accurate and complete 3D models with better spatial consistency and level of detail.