A computer-implemented method for segmentation of 3D image data of a 3D image, the method comprising: • determining local features for each of a plurality of views (101, 102, 103) of the 3D image; • determining a local feature graph based on the determined local features, wherein the local feature graph comprises a plurality of vertexes relating to the local features, wherein each local feature is a compact description of a patch surrounding a point of the 3D image and each vertex is linked to a predetermined number of spatially adjacent vertexes, and the determining the local feature graph comprises assigning an edge weight to each edge between two vertexes; • quantising a depth map of the 3D image data, the depth map obtained from an external device or by computing based on the 3D image data; • identifying depth regions by determining contiguous depth map elements having a same quantised depth value; • identifying texture regions comprising consistent pixels in the 3D image based on the 3D image data; • for each of the texture regions among the identified texture regions: o calculating a confidence value for the texture region by using the following formula: confidence value = nDR − 1 nFeat wherein nDR is a number of depth regions covered by the texture region and nFeat is a number of local features of the local feature graph in the texture region; ∘ comparing the calculated confidence value with a threshold value; and determining the texture region as an unreliable texture region if the calculated confidence value is below the threshold value and determining the texture region as a reliable texture region if the calculated confidence value is equal to or higher than the threshold value; • eliminating the unreliable texture regions; • computing, for each reliable texture region, a histogram of a local feature distribution among the depth regions covered by the texture region; • segmenting the 3D image into a plurality of depth regions based on the histograms of the reliable texture regions. • determining local features for each of a plurality of views (101, 102, 103) of the 3D image; • determining a local feature graph based on the determined local features, wherein the local feature graph comprises a plurality of vertexes relating to the local features, wherein each local feature is a compact description of a patch surrounding a point of the 3D image and each vertex is linked to a predetermined number of spatially adjacent vertexes, and the determining the local feature graph comprises assigning an edge weight to each edge between two vertexes; • quantising a depth map of the 3D image data, the depth map obtained from an external device or by computing based on the 3D image data; • identifying depth regions by determining contiguous depth map elements having a same quantised depth value; • identifying texture regions comprising consistent pixels in the 3D image based on the 3D image data; • for each of the texture regions among the identified texture regions: o calculating a confidence value for the texture region by using the following formula: confidence value = nDR − 1 nFeat wherein nDR is a number of depth regions covered by the texture region and nFeat is a number of local features of the local feature graph in the texture region; ∘ comparing the calculated confidence value with a threshold value; and determining the texture region as an unreliable texture region if the calculated confidence value is below the threshold value and determining the texture region as a reliable texture region if the calculated confidence value is equal to or higher than the threshold value; o calculating a confidence value for the texture region by using the following formula: confidence value = nDR − 1 nFeat wherein nDR is a number of depth regions covered by the texture region and nFeat is a number of local features of the local feature graph in the texture region; ∘ comparing the calculated confidence value with a threshold value; and determining the texture region as an unreliable texture region if the calculated confidence value is below the threshold value and determining the texture region as a reliable texture region if the calculated confidence value is equal to or higher than the threshold value; • eliminating the unreliable texture regions; • computing, for each reliable texture region, a histogram of a local feature distribution among the depth regions covered by the texture region; • segmenting the 3D image into a plurality of depth regions based on the histograms of the reliable texture regions.