In the domain of point cloud analysis, despite the significant capabilities
of Graph Neural Networks (GNNs) in managing complex 3D datasets, existing
approaches encounter challenges like high computational costs and scalability
issues with extensive scenarios. These limitations restrict the practical
deployment of GNNs, notably in resource-constrained environments. To address
these issues, this study introduce <b>Point<\b> <b>Vi<\b>sion <b>G<\b>NN
(PointViG), an efficient framework for point cloud analysis. PointViG
incorporates a lightweight graph convolutional module to efficiently aggregate
local features and mitigate over-smoothing. For large-scale point cloud scenes,
we propose an adaptive dilated graph convolution technique that searches for
sparse neighboring nodes within a dilated neighborhood based on semantic
correlation, thereby expanding the receptive field and ensuring computational
efficiency. Experiments demonstrate that PointViG achieves performance
comparable to state-of-the-art models while balancing performance and
complexity. On the ModelNet40 classification task, PointViG achieved 94.3%
accuracy with 1.5M parameters. For the S3DIS segmentation task, it achieved an
mIoU of 71.7% with 5.3M parameters. These results underscore the potential and
efficiency of PointViG in point cloud analysis.

PointViG 是一种高效的点云分析框架，使用轻量级图卷积模块提高局部特征聚合效果并减轻过度平滑问题，在大规模点云场景中采用自适应扩张图卷积技术提高计算效率，并且在性能和复杂度方面达到与先进模型相当的表现。