DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Chunghyun | - |
dc.contributor.author | Jeong, Yoonwoo | - |
dc.contributor.author | 조민수 | - |
dc.contributor.author | Park, Jaesik | - |
dc.date.accessioned | 2023-03-06T00:23:22Z | - |
dc.date.available | 2023-03-06T00:23:22Z | - |
dc.date.created | 2023-03-03 | - |
dc.date.issued | 2022-06-24 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/116840 | - |
dc.description.abstract | The recent success of neural networks enables a better interpretation of 3D point clouds, but processing a large-scale 3D scene remains a challenging problem. Most current approaches divide a large-scale scene into small regions and combine the local predictions together. However, this scheme inevitably involves additional stages for pre- and post-processing and may also degrade the final output due to predictions in a local perspective. This paper introduces Fast Point Transformer that consists of a new lightweight self-attention layer. Our approach encodes continuous 3D coordinates, and the voxel hashing-based architecture boosts computational efficiency. The proposed method is demonstrated with 3D semantic segmentation and 3D detection. The accuracy of our approach is competitive to the best voxel-based method, and our network achieves 129 times faster inference time than the state-of-the-art, Point Transformer, with a reasonable accuracy trade-off in 3D semantic segmentation on S3DIS dataset. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.relation.isPartOf | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 | - |
dc.relation.isPartOf | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | - |
dc.title | Fast Point Transformer | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.identifier.bibliographicCitation | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, pp.16928 - 16937 | - |
dc.citation.conferenceDate | 2022-06-19 | - |
dc.citation.conferencePlace | US | - |
dc.citation.endPage | 16937 | - |
dc.citation.startPage | 16928 | - |
dc.citation.title | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 | - |
dc.contributor.affiliatedAuthor | 조민수 | - |
dc.contributor.affiliatedAuthor | Park, Jaesik | - |
dc.identifier.scopusid | 2-s2.0-85132778978 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
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