Efficient Graph Convolutional Networks for Point Cloud Handling
Towards Efficient Graph Convolutional Networks for Point Cloud Handling
This paper focuses on enhancing the computational efficiency of Graph Convolutional Networks (GCNs) when applied to point cloud data. The research analyzes the standard GCN operations, which typically involve a K-nearest neighbor (KNN) search and a multilayer perceptron (MLP), to identify areas for improvement.
Key Findings and Optimizations:
- Smooth Propagation of Local Geometric Information: The study observes that local geometric structure information in 3D representations propagates smoothly through GCNs that utilize KNN searches for neighborhood feature aggregation. This observation leads to the simplification of multiple KNN searches within GCNs.
- Order of Operations: It is found that shuffling the order of graph feature gathering and MLP operations results in equivalent or similar composite operations. This insight allows for optimization of the computational procedure in GCNs.
Optimized GCNs:
Based on these findings, the researchers have optimized the computational procedure in GCNs. The proposed optimizations aim to reduce computational complexity, decrease memory consumption, and accelerate inference speed.
Experimental Results:
A series of experiments demonstrate that the optimized networks achieve these improvements while maintaining comparable accuracy for point cloud learning tasks. The code for this research is available on GitHub.
Research Areas and Labs:
This work falls under the research area of Computer Vision and is associated with the Spatial AI Lab – Zurich.
Publication Details:
Authors:
- Yawei Li
- He Chen
- Zhaopeng Cui
- Radu Timofte
- Marc Pollefeys
- Gregory Chirikjian
- Luc Van Gool
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