Learning the Geodesic Embedding with Graph Neural Networks

• URL: http://arxiv.org/abs/2309.05613v2
• Date: Wed, 4 Oct 2023 09:42:43 GMT
• Title: Learning the Geodesic Embedding with Graph Neural Networks
• Authors: Bo Pang, Zhongtian Zheng, Guoping Wang, Peng-Shuai Wang
• Abstract summary: We present GeGnn, a learning-based method for computing the approximate geodesic distance between two arbitrary points on discrete polyhedra surfaces. Our key idea is to train a graph neural network to embed an input mesh into a high-dimensional embedding space. We verify the efficiency and effectiveness of our method on ShapeNet and demonstrate that our method is faster than existing methods by orders of magnitude.
• Score: 22.49236293942187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present GeGnn, a learning-based method for computing the approximate geodesic distance between two arbitrary points on discrete polyhedra surfaces with constant time complexity after fast precomputation. Previous relevant methods either focus on computing the geodesic distance between a single source and all destinations, which has linear complexity at least or require a long precomputation time. Our key idea is to train a graph neural network to embed an input mesh into a high-dimensional embedding space and compute the geodesic distance between a pair of points using the corresponding embedding vectors and a lightweight decoding function. To facilitate the learning of the embedding, we propose novel graph convolution and graph pooling modules that incorporate local geodesic information and are verified to be much more effective than previous designs. After training, our method requires only one forward pass of the network per mesh as precomputation. Then, we can compute the geodesic distance between a pair of points using our decoding function, which requires only several matrix multiplications and can be massively parallelized on GPUs. We verify the efficiency and effectiveness of our method on ShapeNet and demonstrate that our method is faster than existing methods by orders of magnitude while achieving comparable or better accuracy. Additionally, our method exhibits robustness on noisy and incomplete meshes and strong generalization ability on out-of-distribution meshes. The code and pretrained model can be found on https://github.com/IntelligentGeometry/GeGnn.

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