Traditional　monocular　depth　estimation　assumes　that　all　objects　are　reliably　visible　in　the　RGB　color　domain.　However,　this　is　not　always　the　case　as　more　and　more　buildings　are　decorated　with　transparent　glass　walls.　This　problem　has　not　been　explored　due　to　the　difficulties　in　annotating　the　depth　levels　of　glass　walls,　as　commercial　depth　sensors　cannot　provide　correct　feedbacks　on　transparent　objects.　Furthermore,　estimating　depths　from　transparent　glass　walls　requires　the　aids　of　surrounding　context,　which　has　not　been　considered　in　prior　works.　To　cope　with　this　problem,　we　introduce　the　first　Glass　Walls　Depth　Dataset　(GW-Depth　dataset).　We　annotate　the　depth　levels　of　transparent　glass　walls　by　propagating　the　context　depth　values　within　neighboring　flat　areas,　and　the　glass　segmentation　mask　and　instance　level　line　segments　of　glass　edges　are　also　provided.　On　the　other　hand,　a　tailored　monocular　depth　estimation　method　is　proposed　to　fully　activate　the　glass　wall　contextual　understanding.　First,　we　propose　to　exploit　the　glass　structure　context　by　incorporating　the　structural　prior　knowledge　embedded　in　glass　boundary　line　segment　detections.　Furthermore,　to　make　our　method　adaptive　to　scenes　without　structure　context　where　the　glass　boundary　is　either　absent　in　the　image　or　too　narrow　to　be　recognized,　we　propose　to　derive　a　reflection　context　by　utilizing　the　depth　reliable　points　sampled　according　to　the　variance　between　two　depth　estimations　from　different　resolutions.　High-resolution　depth　is　thus　estimated　by　the　weighted　summation　of　depths　by　those　reliable　points.　Extensive　experiments　are　conducted　to　evaluate　the　effectiveness　of　the　proposed　dual　context　design.　Superior　performances　of　our　method　is　also　demonstrated　by　comparing　with　state-of-the-art　methods.　We　present　the　first　feasible　solution　for　monocular　depth　estimation　in　the　presence　of　glass　walls,　which　can　be　widely　adopted　in　autonomous　navigation.