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GraphFusion: Integrating multi-level semantic information with graph computing for enhanced 3D instance segmentation
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DOI：10.1016/j.neucom.2024.128287.png)
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Abstract
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Graph computing has emerged as a focal point in recent research across various fields, including the realm of 3D instance segmentation, where it aids in detecting and segmenting objects within volumetric data. Our study introduces GraphFusion, a state-of-the-art network that harnesses the power of graph computing to enhance the segmentation of 3D point clouds. GraphFusion is equipped with a Multi-Level Semantic Aggregation Module, architectured akin to a graph, to capture comprehensive features from 3D point clouds. Utilizing graph-based methodologies, this module proficiently aggregates multi-scale semantic information, illuminating insights from both global and local contexts. Additionally, our Parallel Feature Fusion Transformer Module leverages graph- transformer techniques to intricately process complex spatial relationships within point clouds, culminating in a more cohesive feature representation. Rigorous experiments on the ScanNetv2 dataset affirm the dominance of GraphFusion, which eclipses current methods by 2.2% in mean Average Precision (mAP) on the hidden test set. The model's code is accessible at https://github.com/3171228612/GraphFusion.
Keywords:
3D instance segmentation
Graph computing
Transformer network
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