Deep learning technique has yielded significant improvements in point cloud completion with the aim of completing missing object shapes from partial inputs. However, most existing methods fail to recover realistic structures due to over-smoothing of fine-grained details. In this paper, we develop a voxel-based network for point cloud completion by leveraging edge generation (VE-PCN). We first embed point clouds into regular voxel grids, and then generate complete objects with the help of the hallucinated shape edges. This decoupled architecture together with a multi-scale grid feature learning is able to generate more realistic on-surface details. We evaluate our model on the publicly available completion datasets and show that it outperforms existing state-of-the-art approaches quantitatively and qualitatively. Our source code is available at https://github.com/xiaogangw/VE-PCN.
翻译:深度学习技术在点云完成方面有了重大改进,目的是从部分投入中完成缺失的物体形状,然而,由于细细细细节的过度移动,大多数现有方法未能恢复现实的结构。在本文件中,我们通过利用边缘生成(VE-PCN)开发了基于 voxel 的点云完成网络。我们首先将点云嵌入常规 voxel 网格中,然后在致幻形状边缘的帮助下生成完整的对象。这个拆解的架构加上多尺度的网格特征学习能够产生更现实的表面细节。我们评估了我们关于公开提供的完成数据集的模型,并表明它的数量和质量上超过了现有的最先进的方法。我们的源代码可在https://github.com/xiaogw/Ve-PCN上查阅。