Geometric deep learning on non-Euclidean data, particularly point clouds, has in recent years been getting a lot of attention and success. Despite this success, the relationship between points in a delineated subgraph have still not been fully explored - like the under exploration of correlations between inter-class and/or intra-class point connections - leading to suboptimal point cloud segmentations. Thus, this study proposes a scale-invariant graph attention convolution network that has the capacity to dynamically adapt to sub-graph structures at varying scales, and reduce noisy local feature propagation due to mixed object class neighborhoods. The efficacy of the proposed framework is evaluated using the Toronto3D benchmark dataset and attained an mI-oU of 61.2%, outperforming all the existing methods it was compared to.
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Attention-Based Multi-Scale Graph Convolution for Point Cloud Semantic Segmentation
Semantic Scholar · Computer Science · 2022
Abstract
Geometric deep learning on non-Euclidean data, particularly point clouds, has in recent years been getting a lot of attention and success. Despite this success, the relationship between points in a delineated subgraph have still not been fully explored - like the under exploration of correlations between inter-class and/or intra-class point connections - leading to suboptimal point cloud segmentations. Thus, this study proposes a scale-invariant graph attention convolution network that has the capacity to dynamically adapt to sub-graph structures at varying scales, and reduce noisy local feature propagation due to mixed object class neighborhoods. The efficacy of the proposed framework is evaluated using the Toronto3D benchmark dataset and attained an mI-oU of 61.2%, outperforming all the existing methods it was compared to.