The　distortion　introduced　by　different　projection　(e.g.　ERP,　CUBE,　etc)　pose　a　great　challenge　to　the　depth　estimation　task　of　360°　images.　We　propose　a　novel　approach,　named　OlaNet,　to　solve　the　self-supervised　360°　depth　estimation　problem.　Our　method　is　motivated　by　two　aspects:　1)　the　content　of　360°　imagery　can　be　better　learned　by　effective　fields-of-views　technology,　i.e.,　atrous　spatial　pyramid　pooling　combined　with　projection　coordinate　prior;　2)　L1-norm　can　learn　more　robust　and　sparse　representation　than　L2-norm　in　smooth　regularization　of　depth　estimation.　By　considering　these　two　evidence,　we　develop　an　end-to-end　network　that　adopts　the　distortion-aware　view　synthesis,　atrous　spatial　pyramid　pooling　and　L1-norm　regularized　smooth　term,　to　achieve　the　360°　depth　estimation　effectively　and　robustly.　Extensive　experiments　on　the　3D60　dataset　demonstrate　the　superior　performance　of　our　OlaNet　approach　in　comparison　with　the　SOTA　methods.　©　2021　IEEE