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アイテム
Relationship Between Semantic Segmentation Model and Additional Features for 3D Point Clouds Obtained from on-Vehicle LIDAR
http://hdl.handle.net/10228/0002000782
http://hdl.handle.net/10228/00020007828e6d5de0-9b39-4253-bbf2-b0f8ac2ee35c
| 名前 / ファイル | ライセンス | アクション |
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neuro_51.pdf (2.7 MB)
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| アイテムタイプ | 学術雑誌論文 = Journal Article(1) | |||||||||||||
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| 公開日 | 2024-06-17 | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Relationship Between Semantic Segmentation Model and Additional Features for 3D Point Clouds Obtained from on-Vehicle LIDAR | |||||||||||||
| 言語 | en | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 著者 |
Hashimoto, Hisato
× Hashimoto, Hisato
× 榎田, 修一
WEKO
32516
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | The study delves into semantic segmentation’s role in recognizing regions within data, with a focus on images and 3D point clouds. While images from wide-angle cameras are prevalent, they falter in challenging environments like low light. In such cases, LIDAR (Laser Imaging Detection and Ranging), despite its lower resolution, excels. The combination of LIDAR and semantic segmentation proves effective for outdoor environment understanding. However, highly accurate models often demand substantial parameters, leading to computational challenges. Techniques like knowledge distillation and pruning offer solutions, though with possible accuracy trade-offs. This research introduces a strategy to merge feature descriptors, such as reflectance intensity and histograms, into the semantic segmentation model. This process balances accuracy and computational efficiency. The findings suggest that incorporating feature descriptors suits smaller models, while larger models can benefit from optimizi ng computation and utilizing feature descriptors for recognition tasks. Ultimately, this research contributes to the evolution of resource-efficient semantic segmentation models for autonomous driving and similar fields. | |||||||||||||
| 言語 | en | |||||||||||||
| 備考 | ||||||||||||||
| 内容記述タイプ | Other | |||||||||||||
| 内容記述 | The 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, February 27-29, 2024, Rome, Italy | |||||||||||||
| 言語 | en | |||||||||||||
| 書誌情報 |
en : Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications p. 547-553, 発行日 2024 |
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| 出版社 | ||||||||||||||
| 出版者 | ScitePress | |||||||||||||
| 言語 | en | |||||||||||||
| DOI | ||||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.5220/0012374400003660 | |||||||||||||
| ISBN | ||||||||||||||
| 識別子タイプ | ISBN | |||||||||||||
| 関連識別子 | 978-989-758-679-8 | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 2184-4321 | |||||||||||||
| 著作権関連情報 | ||||||||||||||
| 権利情報 | Copyright (c) 2024 by SCITEPRESS – Science and Technology Publications, Lda. Paper published under CC license (CC BY-NC-ND 4.0) | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Semantic Segmentation | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Point Cloud | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Deep Learning | |||||||||||||
| 会議記述 | ||||||||||||||
| 会議名 | The 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | |||||||||||||
| 言語 | en | |||||||||||||
| 回次 | 19 | |||||||||||||
| 開始年 | 2024 | |||||||||||||
| 開始月 | 02 | |||||||||||||
| 開始日 | 27 | |||||||||||||
| 終了年 | 2024 | |||||||||||||
| 終了月 | 02 | |||||||||||||
| 終了日 | 29 | |||||||||||||
| 開催国 | ITA | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | VoR | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||
| 査読の有無 | ||||||||||||||
| 値 | yes | |||||||||||||
