Scalable multi-view stereo using CMA-ES and distance transform-based depth map refinement
Nirmal Sukumaran Nair, Prof. (Dr.) Madhu S. Nair · 2021
Recovering three-dimensional structure from images is a long-standing ill-posed inverse problem in computer vision. This paper presents a simple and highly scalable method to reconstruct dense 3D point cloud from multi-view images by estimating per-pixel depth using an evolutionary computation technique – CMA-ES. The proposed method uses ZNCCbased template matching to reconstruct fine details of textured regions and DAISY-based feature matching to reconstruct smooth surface of homogeneous regions. We handle the problem of reconstructing large homogeneous regions using distance transform-based adaptive median filtering. The proposed method is highly scalable since pixels are processed independently at all stages of reconstruction – depth map estimation, refinement, and fusion. This enables the proposed method to be parallelized at the pixel-level, unlike most existing methods that can only be parallelized at the image-level. Experimental results on Middlebury benchmark dataset demonstrate the robustness and efficacy of the proposed method in reconstructing textured as well as homogeneous regions.