Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143545
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dc.contributor.authorShi, Hanyuen_US
dc.contributor.authorLin, Guoshengen_US
dc.contributor.authorWang, Haoen_US
dc.contributor.authorHung, Tzu-Yien_US
dc.contributor.authorWang, Zhenhuaen_US
dc.date.accessioned2020-09-08T06:55:28Z-
dc.date.available2020-09-08T06:55:28Z-
dc.date.issued2020-
dc.identifier.citationShi, 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/CVPR42600.2020.00463en_US
dc.identifier.urihttps://hdl.handle.net/10356/143545-
dc.description.abstractPoint 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.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationDelta-NTU Corporate Laben_US
dc.relationAISG-RP-2018-003en_US
dc.relationRG22/19 (S)en_US
dc.rights© 2020 The Author(s) (published by IEEE). This is an open-access article distributed under the terms of the Creative Commons Attribution License.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleSpSequenceNet : semantic segmentation network on 4D point cloudsen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.conferenceIEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020en_US
dc.contributor.organizationZhejiang University of Technologyen_US
dc.identifier.doi10.1109/CVPR42600.2020.00463-
dc.description.versionPublished versionen_US
dc.subject.keywordsSegmentationen_US
dc.subject.keywordsComputer Visionen_US
dc.citation.conferencelocationSeattle, Washington, USA.en_US
dc.description.acknowledgementThis work is supported by the Delta-NTU Corporate Lab with funding support from Delta Electronics Inc. and the National Research Foundation (NRF) Singapore. This work is also partly supported by the National Research Foundation Singapore under its AI Singapore Programme (Award Number: AISG-RP-2018-003), the MOE Tier-1 research grant: RG22/19 (S), and the National Natural Science Foundation of China (61802348).en_US
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