Aug 13, 2021 · We find that it is possible to radically simplify these models so long as the feature extraction layer is retained with minimal degradation to model ...

After the first layer, it is possible to radically simplify the architecture to reduce latency and memory con- sumption. 3.2. Experimental Verification. We now ...

We find that it is possible to radically simplify these models so long as the feature extraction layer is retained with minimal degradation to model performance ...

After the first layer, it is possible to radically simplify the architecture to reduce latency and memory con- sumption. 3.2. Experimental Verification. We now ...

Sep 9, 2024 · In the project developed for this research, a detailed 3-D point cloud model is produced by scanning a physical structure with LiDAR. Then, ...

To eliminate redundant operations in GNNs, [5, 11] propose identifying and manually simplifying the model structure through numerous ablation experiments and ...

We aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds. The basic graph convolution that is ...

作者發現一些GNN 的優化方法用在點雲資料上沒有作用，認為點雲類型的資料需要獨特的模型架構，作者認為這是因為點雲資料重度依賴點跟點之間的座標關係，如果在模型的一開始(第 ...

In recent years graph neural network (GNN)-based approaches have become a popular strategy for processing point cloud data, regularly achieving state-of-the-art ...

Do We Need Anisotropic Graph Neural Networks? PDF Code Blog Post · Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification.