#5913. Densely connected graph convolutional network for joint semantic and instance segmentation of indoor point clouds

July 2026publication date
Proposal available till 03-06-2025
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Journal’s subject area:
Engineering (miscellaneous);
Computer Science Applications;
Computers in Earth Sciences;
Atomic and Molecular Physics, and Optics;
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Abstract:
In this paper, a densely connected graph convolutional network is proposed to jointly realize the semantic and instance segmentation of indoor point clouds. We combine a Graph Convolutional Network (GCN) and Multilayer Perceptron (MLP) into a new model (namely GCN-MLP) and design a more efficient attention pooling operation to establish a new and efficient module for extracting point cloud features. Also, we add a point cloud channel aggregation module to aggregate multi-level deep features to better express the discriminative characteristics of indoor point clouds. Next, a framework for joint semantic and instance segmentation is designed on the basis of the above modules. In the framework, the semantic branch and instance branch promote each other and obtain better semantic and instance segmentation effects simultaneously. Besides, a dense connection way among different levels of feature maps is designed, to fully extract the features of indoor scene.
Keywords:
Deep learning; Dense connection; Graph convolutional network; Indoor point clouds; Joint semantic and instance segmentation

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