Owing　to　the　rapid　development　of　emerging　360(degrees)panoramic　imaging　techniques,　indoor　360(degrees)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(degrees)depth　estimation　in　self-supervised　mode.　However,　self-supervised　360　degrees　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(degrees)depthestimation.　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　of360(degrees)images.　In　addition,　two　structural　regularities　of　indoor360(degrees)scenes　are　devised　as　two　additional　supervisory　signals　to　efficiently　optimize　our　self-supervised　360(degrees)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(degrees)imagewith　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(degrees)image.　Experimental　results　illustrate　that　our　proposed　method　achieves　superior　performance　than　the　current　advanced　depth　estimation　methods　on　four　publicly　available　datasets