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We present a novel deep-learning-based method for Multi-View Stereo. Ourmethod estimates high resolution and highly precise depth maps iteratively, bytraversing the continuous space of feasible depth values at each pixel in abinary decision fashion. The decision process leverages a deep-networkarchitecture: this computes a pixelwise binary mask that establishes whethereach pixel actual depth is in front or behind its current iteration individualdepth hypothesis. Moreover, in order to handle occluded regions, at eachiteration the results from different source images are fused using pixelwiseweights estimated by a second network. Thanks to the adopted binary decisionstrategy, which permits an efficient exploration of the depth space, our methodcan handle high resolution images without trading resolution and precision.This sets it apart from most alternative learning-based Multi-View Stereomethods, where the explicit discretization of the depth space requires theprocessing of large cost volumes. We compare our method with state-of-the-artMulti-View Stereo methods on the DTU, Tanks and Temples and the challengingETH3D benchmarks and show competitive results.

IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions