Indoor　scene　understanding　based　on　the　depth　image　data　is　a　cutting-edge　issue　in　the　field　of　three-dimensional　computer　vision.　Taking　the　layout　characteristics　of　the　indoor　scenes　and　more　plane　features　in　these　scenes　into　account,　this　paper　presents　a　depth　image　segmentation　method　based　on　Gauss　Mixture　Model　clustering.　First,　transform　the　Kinect　depth　image　data　into　point　cloud　which　is　in　the　form　of　discrete　three-dimensional　point　data,　and　denoise　and　down-sample　the　point　cloud　data;　second,　calculate　the　point　normal　of　all　points　in　the　entire　point　cloud,　then　cluster　the　entire　normal　using　Gaussian　Mixture　Model,　and　finally　implement　the　entire　point　clouds　segmentation　by　RANSAC　algorithm.　Experimental　results　show　that　the　divided　regions　have　obvious　boundaries　and　segmentation　quality　is　above　normal,　and　lay　a　good　foundation　for　object　recognition.　©　(2013)　Trans　Tech　Publications,　Switzerland.