Estimating　360　degrees　depth　information　has　attracted　considerable　attention　due　to　the　fast　development　of　emerging　360　degrees　cameras.　However,　most　researches　only　focus　on　dealing　with　the　distortion　of　360　degrees　images　without　considering　the　geometric　information　of　360　degrees　images,　leading　to　poor　performance.　In　this　paper,　we　conduct　to　apply　indoor　structure　regularities　for　self-supervised　360　degrees　image　depth　estimation.　Specifically,　we　carefully　design　two　geometric　constraints　for　efficient　model　optimization　including　dominant　direction　normal　constraint　and　planar　consistency　depth　constraint.　The　dominant　direction　normal　constraint　enables　to　align　the　normal　of　indoor　360　degrees　images　with　the　direction　of　vanishing　points.　The　planar　consistency　depth　constraint　is　utilized　to　fit　the　estimated　depth　of　each　pixel　by　its　3D　plane.　Hence,　incorporating　these　two　geometric　constraints　can　further　facilitate　the　generation　of　accurate　depth　results　for　360　degrees　images.　Extensive　experiments　illustrate　that　our　designed　method　improves　delta(1)　by　an　average　of　4.82%　compared　to　state-of-the-art　methods　on　Matterport3D　and　Stanford2D3D　datasets　within　3D60.