Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143545
Title: SpSequenceNet : semantic segmentation network on 4D point clouds
Authors: Shi, Hanyu
Lin, Guosheng
Wang, Hao
Hung, Tzu-Yi
Wang, Zhenhua
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Shi, H., Lin, G., Wang, H., Hung, T.-Y., & Wang, Z. (2020). SpSequenceNet : semantic segmentation network on 4D point clouds. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020. doi:10.1109/CVPR42600.2020.00463
Project: Delta-NTU Corporate Lab
AISG-RP-2018-003
RG22/19 (S)
Abstract: Point clouds are useful in many applications like autonomous driving and robotics as they provide natural 3D information of the surrounding environments. While there are extensive research on 3D point clouds, scene understanding on 4D point clouds, a series of consecutive 3D point clouds frames, is an emerging topic and yet underinvestigated. With 4D point clouds (3D point cloud videos), robotic systems could enhance their robustness by leveraging the temporal information from previous frames. However, the existing semantic segmentation methods on 4D point clouds suffer from low precision due to the spatial and temporal information loss in their network structures. In this paper, we propose SpSequenceNet to address this problem. The network is designed based on 3D sparse convolution, and it includes two novel modules, a cross-frame global attention module and a cross-frame local interpolation module, to capture spatial and temporal information in 4D point clouds. We conduct extensive experiments on SemanticKITTI, and achieve the state-of-the-art result of 43.1% on mIoU, which is 1.5% higher than the previous best approach.
URI: https://hdl.handle.net/10356/143545
DOI: 10.1109/CVPR42600.2020.00463
Rights: © 2020 The Author(s) (published by IEEE). This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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