Most　man-made　indoor　and　urban　scenes　are　composed　of　a　set　of　orthogonal　and　parallel　planes.　In　robotics　and　computer　vision,　these　scenes　typically　represented　by　the　Manhattan-World　model.　The　accurate　estimation　of　the　Manhattan　Frame,　which　consists　of　three　orthogonal　directions　being　used　to　represent　the　Manhattan-World,　plays　an　important　role　in　many　applications,　such　as　SLAM,　scene　understanding　and　3D　reconstruction.　In　this　paper,　a　new　method　for　accurately　recovering　the　Manhattan　frame　from　a　single　RGB-D　image　by　using　the　orientation　relevance　is　proposed.　It　first　extracts　planes　from　the　input　single　RGB-D　image.　Then　three　orthogonal　dominant　planes　are　determined　by　introducing　the　concept　of　orientation　relevance.　Finally,　the　Manhattan　Frame　can　be　easily　recovered　from　the　obtained　three　orthogonal　dominant　planes.　Experiments　with　open　dataset　validate　the　proposed　method.　The　overall　performance　of　the　proposed　method,　which　takes　both　accuracy　and　speed　into　account,　is　superior　to　that　of　the　state-of-the-art　methods.　It　is　also　applied　on　the　application　of　scene　annotation　to　confirm　its　applicability.