Si trova su / Altri legami
© 1981–2012 IEEE.Shape completion for 3–D point clouds is an important issue in the literature of computer graphics and computer vision. We propose an end–to–end shape–preserving point completion network through encoder–decoder architecture, which works directly on incomplete 3–D point clouds and can restore their overall shapes and fine–scale structures. To achieve this task, we design a novel encoder that encodes information from neighboring points in different orientations and scales, as well as a decoder that outputs dense and uniform complete point clouds. We augment a 3–D object dataset based on ModelNet40 and validate the effectiveness of our shape–preserving completion network. Experimental results demonstrate that the recovered point clouds lie close to ground truth points. Our method outperforms state–of–the–art approaches in terms of Chamfer distance (CD) error and earth mover's distance (EMD) error. Furthermore, our end–to–end completion network is robust to model noise, the different levels of incomplete data, and can also generalize well to unseen objects and real–world data.
