Depth　maps　have　been　widely　used　in　many　real　world　applications,　such　as　human-computer　interaction　and　virtual　reality.　However,　due　to　the　limitation　of　current　depth　sensing　technology,　the　captured　depth　maps　usually　suffer　from　low　resolution　and　insufficient　quality.　In　this　paper,　we　propose　a　depth　map　super-resolution　method　via　joint　local　gradient　and　nonlocal　structural　regularizations.　Depth　maps　contain　mainly　smooth　areas　separated　by　textures　which　demonstrate　distinct　geometry　direction　characteristic.　Motivated　by　this,　we　classify　depth　map　patches　according　to　their　geometrical　directions　and　learn　a　compact　online　dictionary　in　each　class.　We　further　introduce　two　regularization　terms　into　the　sparse　representation　framework.　Firstly,　a　multi-directional　total　variation　model　is　proposed　to　characterize　the　local　patterns　in　the　gradient　domain.　Secondly,　a　nonlocal　autoregressive　model　is　introduced　to　provide　nonlocal　constraint　to　the　local　structures,　which　can　effectively　restore　image　details　and　suppress　noise.　Quantitative　and　qualitative　evaluations　compared　with　state-of-the-art　methods　demonstrate　that　the　proposed　method　achieves　superior　performance　for　various　configurations　of　magnification　factors　and　datasets.