Extending Convolution to Semantic Segmentation with Graph Neural Networks

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Abstract Scene understanding enables multiple applications such as robotic automation. To enable scene understanding, semantic segmentation is an essential component. Highly accurate per-voxel semantic labels can efficiently be obtained through the use of encoder-decoder Convolutional Neural Networks (CNNs). However, scene understanding requires instances of objects to be discovered in a scene. Per-voxel labelling is unable to distinguish between separate instances of a given object. A novel method is proposed that enables segment-level predictions to be formed from the voxel-level predictions provided by the CNN architecture. This method uses an existing 3D CNN architecture to provide rich and computationally inexpensive features. The scene is then segmented with a graph-based segmentation technique to identify objects in the scene. Finally, a graph-based neural network is used to capture hierarchical information improving overall accuracy. The approach is tested on the S3DIS and ScanNet datasets to provide a comparable baseline to more modern architectures.

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