Point cloud is one of the widely used techniques for representing and storing 3D geometric data. In the past several methods have been proposed for processing point clouds. Methods such as PointNet and FoldingNet have shown promising results for tasks like 3D shape classification and segmentation. This work proposes a tree-structured autoencoder framework to generate robust embeddings of 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 几何数据广泛使用的技术之一。 过去曾提出过处理点云的几种方法。 诸如 PointNet 和 FoldingNet 等方法已经为 3D 形状分类和分解等任务展示了有希望的结果。 这项工作提议了一个树结构自动编码框架, 以便利用图形组合的等级信息产生强大的点云嵌入。 我们进行了多次实验, 以评估拟议编码器结构产生的嵌入质量, 并对 t- SNE 地图进行可视化, 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应用中的适用性, 如 3D 点云完成和单一图像3D 重建 。