Abstract
A novel algorithm to detect an optimal set of semantic lines is proposed in this work. We develop two networks: selection network (S-Net) and harmonization network (H-Net). First, S-Net computes the probabilities and offsets of line candidates. Second, we filter out irrelevant lines through a selection-and-removal process. Third, we construct a complete graph, whose edge weights are computed by H-Net. Finally, we determine a maximal weight clique representing an optimal set of semantic lines. Moreover, to assess the overall harmony of detected lines, we propose a novel metric, called HIoU. Experimental results demonstrate that the proposed algorithm can detect harmonious semantic lines effectively and efficiently. Our codes are available at https://github.com/dongkwonjin/Semantic-Line-MWCS.
| Original language | English |
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| Title of host publication | Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 |
| Publisher | IEEE Computer Society |
| Pages | 16732-16740 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781665445092 |
| DOIs | |
| Publication status | Published - 2021 |
| Event | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 - Virtual, Online, United States Duration: 2021 Jun 19 → 2021 Jun 25 |
Publication series
| Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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| ISSN (Print) | 1063-6919 |
Conference
| Conference | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 |
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| Country/Territory | United States |
| City | Virtual, Online |
| Period | 21/6/19 → 21/6/25 |
Bibliographical note
Funding Information:This work was supported in part by the National Research Foundation of Korea (NRF) through the Korea Government (MSIT) under grant NRF-2018R1A2B3003896 and in part by the 42dot Inc.
Publisher Copyright:
© 2021 IEEE
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition