TwinAMFNet : Twin Attention-based Multi-modal Fusion Network for 3D Semantic Segmentation

Jaegeun Yoon, Jiyeon Jeon, Kwangho Song

http://doi.org/10.5626/JOK.2023.50.9.784

Recently, with the increase in the number of accidents due to misrecognition in autonomous driving, interest in 3D semantic segmentation based on sensor fusion using multi-modal sensors has increased. Accordingly, this study introduces TwinAMFNet, a novel 3D semantic segmentation neural network through sensor fusion of RGB cameras and LiDAR. The proposed neural network includes a twin neural network that processes RGB images and point cloud projection images projected on a 2D coordinate plane and through an attention-based fusion module for feature step fusion in the encoder and decoder. The proposed method shows improvement of further extended object and boundary classification. As a result, the proposed neural network recorded approximately 68% performance in 3D semantic segmentation based on mIoU, and showed approximately 4.5% improved performance compared to the ones reported in the existing studies.