PointViG: A Lightweight GNN-based Model for Efficient Point Cloud Analysis
This addresses efficiency challenges for deploying GNNs in resource-constrained environments like robotics or autonomous systems, though it is incremental as it builds on existing GNN methods.
The paper tackled the problem of high computational costs and scalability issues in point cloud analysis using Graph Neural Networks (GNNs), resulting in PointViG, a lightweight model that achieved 94.3% accuracy on ModelNet40 classification with 1.5M parameters and 71.7% mIoU on S3DIS segmentation with 5.3M parameters.
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.