Owing　to　the　rapid　development　of　emerging　360°　panoramic　imaging　techniques,　indoor　360°　depth　estimation　has　aroused　extensive　attention　in　the　community.　Due　to　the　lack　of　available　ground　truth　depth　data,　it　is　extremely　urgent　to　model　indoor　360°　depth　estimation　in　self-supervised　mode.　However,　self-supervised　360°　depth　estimation　suffers　from　two　major　limitations.　One　is　the　distortion　and　network　training　problems　caused　by　Equirectangular　projection　(ERP),　and　the　other　is　that　texture-less　regions　are　quite　difficult　to　back-propagate　in　self-supervised　mode.　Hence,　to　address　the　above　issues,　we　introduce　spherical　view　synthesis　for　learning　self-supervised　360°　depth　estimation.　Specifically,　to　alleviate　the　ERP-related　problems,　we　first　propose　a　dual-branch　distortion-aware　network　to　produce　the　coarse　depth　map,　including　a　distortion-aware　module　and　a　hybrid　projection　fusion　module.　Subsequently,　the　coarse　depth　map　is　utilized　for　spherical　view　synthesis,　in　which　a　spherically　weighted　loss　function　for　view　reconstruction　and　depth　smoothing　is　investigated　to　optimize　the　projection　distribution　problem　of　360°　images.　In　addition,　two　structural　regularities　of　indoor　360°　scenes　are　devised　as　two　additional　supervisory　signals　to　efficiently　optimize　our　self-supervised　360°　depth　estimation　model,　containing　the　principal-direction　normal　constraint　and　the　co-planar　depth　constraint.　The　principal-direction　normal　constraint　is　designed　to　align　the　normal　of　the　360°　image　with　the　direction　of　the　vanishing　points.　Meanwhile,　we　employ　the　co-planar　depth　constraint　to　fit　the　estimated　depth　of　each　pixel　through　its　3D　plane.　Finally,　a　depth　map　is　obtained　for　the　360°　image.　Experimental　results　illustrate　that　our　proposed　method　achieves　superior　performance　than　the　current　advanced　depth　estimation　methods　on　four　publicly　available　datasets.　　©　1999-2012　IEEE.