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Three-dimensional (3D) image reconstruction is an important field of computer vision for restoring the 3D geometry of a given scene. Due to the demand for large amounts of memory, prevalent methods of 3D reconstruction yield inaccurate results, because of which the highly accuracy reconstruction of a scene remains an outstanding challenge. This study proposes a cascaded depth residual inference network, called DRI-MVSNet, that uses a cross-view similarity-based feature map fusion module fordoi:10.1371/journal.pone.0264721 pmid:35320265 pmcid:PMC8942269 fatcat:nyrzqymhwvcqtmzxatwsbqtn7q