Point cloud is an efficient way of representing and storing 3D geometric data. Deep learning algorithms on point clouds are time and memory efficient. Several methods such as PointNet and FoldingNet have been proposed for processing point clouds. This work proposes an autoencoder based framework to generate robust embeddings for point clouds by utilizing hierarchical information using graph convolution. We perform multiple experiments to assess the quality of embeddings generated by the proposed encoder architecture and visualize the t-SNE map to highlight its ability to distinguish between different object classes. We further demonstrate the applicability of the proposed framework in applications like: 3D point cloud completion and Single image based 3D reconstruction.
翻译:点云是代表并存储 3D 几何数据的一种有效方式。 点云的深学习算法是时间和记忆效率的。 提出了处理点云的几种方法, 如 PointNet 和 FoldingNet 。 这项工作提出了一个基于自动编码器的框架, 以便利用图形混成的等级信息为点云生成强有力的嵌入。 我们进行了多项实验, 以评估拟议编码器结构生成的嵌入质量, 并直观地显示 t- SNE 地图, 以突出其区分不同对象类别的能力。 我们还进一步展示了拟议框架在3D 点云的完成和基于3D 重建的单一图像等应用中的适用性 。