Solving　the　depth　estimation　problem　in　a　360　degrees　image　space,　which　has　holistic　scene　perception,　has　become　a　trend　in　recent　years.　However,　depth　estimation　in　common　360　degrees　images　is　prone　to　geometric　distortion.　Therefore,　this　study　proposes　a　new　method,　CAPDepth,　to　address　the　geometric-distortion　problem　of　360　degrees　monocular　depth　estimation.　We　reduce　the　tangential　projections　by　an　optimized　content-aware　projection　(CAP)　and　a　geometric　embedding　module　to　capture　more　features　for　global　depth　consistency.　Additionally,　we　adopt　an　index　map　and　a　de-blocking　scheme　to　improve　the　inference　efficiency　and　quality　of　our　CAPDepth　model.　Our　experiments　show　that　CAPDepth　greatly　alleviates　the　distortion　problem,　producing　smoother,　more　accurate　predicted　depth　results,　and　improves　performance　in　panoramic　depth　estimation.