TY - GEN
T1 - Applying Bilateral Guided Multi-Viewed Fusion on Asymmetrical 3D Convolution Networks for 3D LiDAR semantic segmentation
AU - Tran, Tai Huu Phuong
AU - Jeon, Jae Wook
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We present a novel approach for 3D semantic segmentation using LiDAR sensor. In this work, we focus on improving the accuracy of the cylindrical partition and asymmetrical 3D convolution networks with a modification on their dimension-decomposition based context modeling module and asymmetrical residual block. The initial version simply performs multiplication and addition operators to combine two feature branches. In our modification, we apply the bilateral guided multi-viewed fusion module to provide better features for the classification stages. We trained and tested our model on SemanticKITTI dataset, our implementation improves better accuracy than the default asymmetrical 3D convolution networks, in which uses cylindrical voxel for the point cloud representation.
AB - We present a novel approach for 3D semantic segmentation using LiDAR sensor. In this work, we focus on improving the accuracy of the cylindrical partition and asymmetrical 3D convolution networks with a modification on their dimension-decomposition based context modeling module and asymmetrical residual block. The initial version simply performs multiplication and addition operators to combine two feature branches. In our modification, we apply the bilateral guided multi-viewed fusion module to provide better features for the classification stages. We trained and tested our model on SemanticKITTI dataset, our implementation improves better accuracy than the default asymmetrical 3D convolution networks, in which uses cylindrical voxel for the point cloud representation.
KW - 3D Semantic Segmentation
KW - Asymmetrical 3D Convolution Network
KW - Bilateral Guided Multi-Viewed Fusion
UR - https://www.scopus.com/pages/publications/85143788890
U2 - 10.1109/ICCE-Asia57006.2022.9954884
DO - 10.1109/ICCE-Asia57006.2022.9954884
M3 - Conference contribution
AN - SCOPUS:85143788890
T3 - 2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022
BT - 2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022
Y2 - 26 October 2022 through 28 October 2022
ER -