Abstract: In order to learn spatial features of 3D shapes more effectively by deep neural networks,and have a better performance for shape segmentation,a boundary-aware point-based deep neural network is proposed for shape segmentation.At first,meshes are transformed into points via a boundary-aware method,so that shape segmentation can be treated as a point labelling problem.Then the points are resampled by slicing them into several subsets.At last,point-based deep neural network different kernel size filters are proposed to capture the spatial information from point cloud,and shapes are finally segmented through each point and its related mesh labelled.The experiments on ShapeNetCore datasets show that the proposed approach can obviously improve the accuracy of 3D shape segmentation,and has the boundary-aware property so that over-segmentation is evitable.