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Many practical systems for image-based surface reconstruction employ astereo/multi-stereo paradigm, due to its ability to scale for large scenes andits ease of implementation for out-of-core operations. In this process,multiple and abundant depth maps from stereo matching must be combined andfused into a single, consistent, and clean point cloud. However, the noises andoutliers caused by stereo matching and the heterogenous geometric errors of theposes present a challenge for existing fusion algorithms, since they mostlyassume Gaussian errors and predict fused results based on data from localspatial neighborhoods, which may inherit uncertainties from multiple depthsresulting in lowered accuracy. In this paper, we propose a novel depth fusionparadigm, that instead of numerically fusing points from multiple depth maps,selects the best depth map per point, and combines them into a single and cleanpoint cloud. This paradigm, called select-and-combine (SAC), is achievedthrough modeling the point level fusion using local Markov Netlets, amicro-network over point across neighboring views for depth/view selection,followed by a Netlets collapse process for point combination. The MarkovNetlets are optimized such that they can inherently leverage spatialconsistencies among depth maps of neighboring views, thus they can addresserrors beyond Gaussian ones. Our experiment results show that our approachoutperforms existing depth fusion approaches by increasing the F1 score thatconsiders both accuracy and completeness by 2.07% compared to the best existingmethod. Finally, our approach generates clearer point clouds that are 18% lessredundant while with a higher accuracy before fusion

Select-and-Combine (SAC): A Novel Multi-Stereo Depth Fusion Algorithm for Point Cloud Generation via Efficient Local Markov Netlets