3D Data Segmentation by Local Classification and Markov Random Fields

Object segmentation in 3D data such as 3D meshes and range maps is an emerging topic attracting increasing research interest. This work proposes a novel method to perform segmentation relying on the use of 3D features. The deployment of a specific grouping algorithm based on a Markov Random Field model successively to classification allows at the same time yielding automatic segmentation of 3D data as well as deploying non-linear classifiers that can well adapt to the data characteristics. Moreover, we embed our approach in a framework that jointly exploits shape and texture information to improve the outcome of the segmentation stage. In addition to quantitative results on several 3D and 2.5D scenes, we also demonstrate the effectiveness of our approach on an online framework based on a stereo sensor.