Dilated Nearest-Neighbor Encoding for 3D Semantic Segmentation of Point Clouds
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Three dimensional (3D) semantic segmentation is important in many scenarios, such as automatic driving, robotic navigation, etc. Random point sampling proves to be computation and memory efficient to tackle large-scale point clouds in semantic segmentation. However, information of small objects or the edge of objects may be lost. Instead of down-sampling point cloud directly, in this paper we propose a dilated nearest-neighbor encoding module to enlarge the receptive field to learn more 3D geometric information. To further reduce the layers of previous neural networks, we designed a multi-level hierarchical feature fusion network. We present one end-to-end 3D semantic segmentation framework based on the backbone of RandLA-Net and the two key components, dilated convolution and efficient feature fusion. Experiments on the benchmark 3D dataset prove that our framework performs better than other state-of-the-art approaches with fewer network layers.