TreeGCN-ED: Encoding Point Cloud using a Tree-Structured Graph Network
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 37 references that are in the map. First posted on arXiv 7 Oct 2021.
Builds on
23 of 37 references.
References · 23
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11 more
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