A method for detecting tooth wear, comprising steps of: receiving, by a processor, first and second digital 3D models of teeth, wherein the first digital 3D model was taken a first time, and the second digital 3D model was taken at a second time later than the first time; segmenting the first digital 3D model of teeth to separate individual teeth within the first digital 3D model of teeth and generate a segmented first digital 3D model of teeth, comprising: performing a first segmentation method for the first digital 3D model of teeth; wherein the first segmentation method comprises a geometric hill-climbing method; and performing a second segmentation method, different from the first segmentation method, for the first digital 3D model of teeth; repeating the segmenting step for the second digital 3D model of teeth to separate individual teeth within the second digital 3D model of teeth and generate a segmented second digital 3D model of teeth; and comparing the segmented first digital 3D model of teeth with the segmented second digital 3D model of teeth to detect tooth wear by determining differences between the segmented first digital 3D model of teeth and the segmented second digital 3D model of teeth where the differences relate to a same tooth, wherein the segmenting step comprises: performing the first segmentation method that over-segments at least some of the teeth within the first digital 3D model of teeth; performing the second segmentation method that classifies points within the first digital 3D model of teeth as being either on an interior of a tooth or on a boundary between teeth in the first digital 3D model of teeth or approximates interstitial spaces between each pair of teeth within the first digital 3D model of teeth or receives user-entered estimations of centroids of teeth in the first digital 3D model of teeth; and combining results of the first and second segmentation methods to generate the segmented first digital 3D model of teeth. receiving, by a processor, first and second digital 3D models of teeth, wherein the first digital 3D model was taken a first time, and the second digital 3D model was taken at a second time later than the first time; segmenting the first digital 3D model of teeth to separate individual teeth within the first digital 3D model of teeth and generate a segmented first digital 3D model of teeth, comprising: performing a first segmentation method for the first digital 3D model of teeth; wherein the first segmentation method comprises a geometric hill-climbing method; and performing a second segmentation method, different from the first segmentation method, for the first digital 3D model of teeth; performing a first segmentation method for the first digital 3D model of teeth; wherein the first segmentation method comprises a geometric hill-climbing method; and performing a second segmentation method, different from the first segmentation method, for the first digital 3D model of teeth; repeating the segmenting step for the second digital 3D model of teeth to separate individual teeth within the second digital 3D model of teeth and generate a segmented second digital 3D model of teeth; and comparing the segmented first digital 3D model of teeth with the segmented second digital 3D model of teeth to detect tooth wear by determining differences between the segmented first digital 3D model of teeth and the segmented second digital 3D model of teeth where the differences relate to a same tooth, wherein the segmenting step comprises: performing the first segmentation method that over-segments at least some of the teeth within the first digital 3D model of teeth; performing the second segmentation method that classifies points within the first digital 3D model of teeth as being either on an interior of a tooth or on a boundary between teeth in the first digital 3D model of teeth or approximates interstitial spaces between each pair of teeth within the first digital 3D model of teeth or receives user-entered estimations of centroids of teeth in the first digital 3D model of teeth; and combining results of the first and second segmentation methods to generate the segmented first digital 3D model of teeth. performing the first segmentation method that over-segments at least some of the teeth within the first digital 3D model of teeth; performing the second segmentation method that classifies points within the first digital 3D model of teeth as being either on an interior of a tooth or on a boundary between teeth in the first digital 3D model of teeth or approximates interstitial spaces between each pair of teeth within the first digital 3D model of teeth or receives user-entered estimations of centroids of teeth in the first digital 3D model of teeth; and classifies points within the first digital 3D model of teeth as being either on an interior of a tooth or on a boundary between teeth in the first digital 3D model of teeth or approximates interstitial spaces between each pair of teeth within the first digital 3D model of teeth or receives user-entered estimations of centroids of teeth in the first digital 3D model of teeth; and combining results of the first and second segmentation methods to generate the segmented first digital 3D model of teeth.