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Point cloud semantic segmentation, which contributes to scene understanding at different scales, is crucial for three-dimensional reconstruction and digital twin cities. However, current semantic segmentation methods mostly extract multi-scale features by down-sampling operations, but the feature maps only have a single receptive field at the same scale, resulting in the misclassification of objects with spatial similarity. To effectively capture the geometric features and the semantic information of different receptive fields, a multi-scale voxel-point adaptive fusion network (MVP-Net) is proposed for point cloud semantic segmentation in urban scenes. First, a multi-scale voxel fusion module with gating mechanism is designed to explore the semantic representation ability of different receptive fields. Then, a geometric self-attention module is constructed to deeply fuse fine-grained point features with coarse-grained voxel features. Finally, a pyramid decoder is introduced to aggregate context information at different scales for enhancing feature representation. The proposed MVP-Net was evaluated on three datasets, Toronto3D, WHU-MLS, and SensatUrban, and achieved superior performance in comparison to the state-of-the-art (SOTA) methods. For the public Toronto3D and SensatUrban datasets, our MVP-Net achieved a mIoU of 84.14% and 59.40%, and an overall accuracy of 98.12% and 93.30%, respectively.
Li et al. (Mon,) studied this question.