Depth　difference,　as　a　popularly　used　feature　for　characterizing　pairwise　pixels　of　range　images,　fails　to　precisely　capture　skeleton　joints　when　human　body　possesses　a　wild　and　complicated　articulation.　As　the　geodesic　distance　of　pairwise　pixels　is　able　to　present　a　global　connected　property　and　adjacent　pixels　often　belong　to　the　same　body　component,　we　propose　an　effective　and　efficient　framework　for　pose　estimation　from　range　images.　Firstly,　all　the　pixels　of　a　range　image　are　grouped　into　superpixels　using　an　improved　Simple　Linear　Iterative　Clustering　algorithm.　Secondly,　those　superpixels　are　labelled　as　the　components　of　a　human　body　using　the　hybrid　feature.　Thirdly,　componentwise　cluster　feature　extraction　is　undertaken　on　skeleton　joints　of　body　components　with　K-means　clustering　algorithm.　Finally,　the　feature　points　of　each　component　are　then　stacked　as　a　compact　representation　of　human　poses　and　mapped　to　the　skeleton　joints　of　a　human　body.　Experimental　results　demonstrate　that　the　proposed　framework　outperforms　several　state-of-the-art　pose　estimation　methods.　(C)　2019　Elsevier　Inc.　All　rights　reserved.