Sep 7, 2022 · We propose a novel Low-level Graph Convolution (LGConv) to process point cloud, which combines the low-level geometric edge feature and high- ...
As cloud environments grow in scale and complexity, efficient load balancing mechanisms become increasingly vital. This paper presents a comprehensive review of ...
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May 28, 2024 · The experiment results show that Differ-GCN performs excellent in object classification and part segmentation. The processing speed of Differ- ...
Abstract: Enlightened by the success of graph neural network, recent graph-based methods achieve impressive performance in point cloud processing.
May 11, 2023 · The mixed attention mechanism is designed to integrate channel attention within DGCB and multi-head attention between DGCB to make the network ...
Jan 20, 2024 · Figure 1: First row: Constructing the ground truth graph for our self-supervised hop distance reconstruction task. (a): Voxelizing the point ...
We aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds. The basic graph convolution that is ...
GC-MLP: Graph Convolution MLP for Point Cloud Analysis - PMC - NCBI
www.ncbi.nlm.nih.gov › PMC9737718
Dec 5, 2022 · Lin et al. [13] proposed a 3D graph convolutional network for processing 3D point cloud data. The shape and weight of the convolution kernel are ...
We propose a deep neural network based on graph-convolutional layers that can elegantly deal with the permutation- invariance problem encountered by learning- ...
Figure 1: Network architecture with two levels of graph pooling. Low-level features are combined with concatenated high-level features via skip connections.