DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Sangjin | ko |
dc.contributor.author | Kim, Sangyeob | ko |
dc.contributor.author | Lee, Juhyoung | ko |
dc.contributor.author | Yoo, Hoi-Jun | ko |
dc.date.accessioned | 2020-12-15T08:10:23Z | - |
dc.date.available | 2020-12-15T08:10:23Z | - |
dc.date.created | 2020-12-01 | - |
dc.date.created | 2020-12-01 | - |
dc.date.created | 2020-12-01 | - |
dc.date.issued | 2020-10-21 | - |
dc.identifier.citation | IEEE International Symposium on Circuits and Systems (ISCAS) | - |
dc.identifier.issn | 0271-4302 | - |
dc.identifier.uri | http://hdl.handle.net/10203/278508 | - |
dc.description.abstract | The graph convolutional network (GCN) based 3D point cloud semantic segmentation (PCSS) processor for mobile devices is proposed. GCN based 3D PCSS requires a lot of computation, making it unsuitable for real-time operation in mobile devices. For real-time 3D PCSS on mobile devices, this paper proposes two key features: 1) a sparse grouping based dilated graph convolution (SG-DGC) which reduces 71.7% of the overall computation of GCN by simply dividing input point cloud into multiple sparse point cloud. 2) group-level pipelining which improves low pipeline utilization due to the computation imbalance of GCN. Finally, the proposed GCN processor is simulated in 65 nm CMOS technology and occupies 4.0 mm 2 . The proposed processor consumes 176mW and shows 54.7 frames-per-second (fps) for the 3D point cloud semantic segmentation of indoor scene with 4k points. | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | A 54.7 fps 3D Point Cloud Semantic Segmentation Processor with Sparse Grouping Based Dilated Graph Convolutional Network for Mobile Devices | - |
dc.type | Conference | - |
dc.identifier.wosid | 000706854700282 | - |
dc.identifier.scopusid | 2-s2.0-85091288256 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | IEEE International Symposium on Circuits and Systems (ISCAS) | - |
dc.identifier.conferencecountry | SP | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1109/ISCAS45731.2020.9181100 | - |
dc.contributor.localauthor | Yoo, Hoi-Jun | - |
dc.contributor.nonIdAuthor | Kim, Sangjin | - |
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