🍩 Database of Original & Non-Theoretical Uses of Topology

1. Persistent Homology Based Graph Convolution Network for Fine-Grained 3D Shape Segmentation (2021)

   Chi-Chong Wong, Chi-Man Vong

   Abstract

   Fine-grained 3D segmentation is an important task in 3D object understanding, especially in applications such as intelligent manufacturing or parts analysis for 3D objects. However, many challenges involved in such problem are yet to be solved, such as i) interpreting the complex structures located in different regions for 3D objects; ii) capturing fine-grained structures with sufficient topology correctness. Current deep learning and graph machine learning methods fail to tackle such challenges and thus provide inferior performance in fine-grained 3D analysis. In this work, methods in topological data analysis are incorporated with geometric deep learning model for the task of fine-grained segmentation for 3D objects. We propose a novel neural network model called Persistent Homology based Graph Convolution Network (PHGCN), which i) integrates persistent homology into graph convolution network to capture multi-scale structural information that can accurately represent complex structures for 3D objects; ii) applies a novel Persistence Diagram Loss (ℒPD) that provides sufficient topology correctness for segmentation over the fine-grained structures. Extensive experiments on fine-grained 3D segmentation validate the effectiveness of the proposed PHGCN model and show significant improvements over current state-of-the-art methods.

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   • Fine-grained 3D segmentation
   • Industry
   • Segmentation
   • 3D fine-grained object
   • Point cloud
   • Convolution networks
   • Machine learning
   • Persistence diagram loss
   • Persistent homology