| Title |
S-PointNet: A New Semantic Segmentation Algorithm Based on PointNet Architecture |
| Authors |
(Jiongyi Meng) ; (Su-il Choi) |
| DOI |
https://doi.org/10.5573/IEIESPC.2021.10.3.204 |
| Keywords |
PointNet; PointSIFT; Point cloud; Semantic segmentation |
| Abstract |
Recently, computer vision studies focusing on 3D comprehension have shown that it is possible to extract features directly from point cloud data. This ability requires an efficient shape-pattern description of point clouds. We designed a semantic segmentation algorithm for point clouds based on the PointNet architecture. Our approach also applies the PointSIFT module, which can encode information in different directions and adapt to the proportions of the shape being considered. Experiments using a standard benchmark dataset show that our algorithm is superior to the PointNet algorithm for semantic segmentation. |