ODD-M3D: Object-Wise Dense Depth Estimation for Monocular 3D Object Detection
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Chanyeong | - |
dc.contributor.author | Kim, Heegwang | - |
dc.contributor.author | Jang, Junbo | - |
dc.contributor.author | Paik, Joonki | - |
dc.date.accessioned | 2024-03-20T03:00:23Z | - |
dc.date.available | 2024-03-20T03:00:23Z | - |
dc.date.issued | 2024-02 | - |
dc.identifier.issn | 0098-3063 | - |
dc.identifier.issn | 1558-4127 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/72952 | - |
dc.description.abstract | Despite the significant benefits of low cost and scalability associated with monocular 3D object detection, accurately estimating depth from a single 2D image remains challenging due to the typical ill-posed nature of the problem. To address this issue, we propose a new method that improves depth estimation accuracy by randomly sampling object-wise points instead of relying on a single center point, which is a common practice in conventional methods. To generate the object-wise multiple reference points, we create a sampling space and obtain the ground truth by moving them from the sampling space to the object space. For this reason, the proposed approach is named ODD-M3D, which stands for Object-wise Dense Depth estimation for Monocular 3D object detection. In addition, we conduct an ablation study comparing LiDAR-guided and random sampling methods to identify the limitations of using point cloud data for image-based 3D object detection tasks. The proposed network achieved better performance by allowing for dense depth estimation instead of sparse depth estimation, which is typical in conventional networks. Authors | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | ODD-M3D: Object-Wise Dense Depth Estimation for Monocular 3D Object Detection | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TCE.2024.3366763 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Consumer Electronics, v.70, no.1, pp 646 - 655 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 001244813900262 | - |
dc.identifier.scopusid | 2-s2.0-85185388547 | - |
dc.citation.endPage | 655 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 646 | - |
dc.citation.title | IEEE Transactions on Consumer Electronics | - |
dc.citation.volume | 70 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
dc.subject.keywordAuthor | Estimation | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Laser radar | - |
dc.subject.keywordAuthor | Location awareness | - |
dc.subject.keywordAuthor | Monocular 3D object detection | - |
dc.subject.keywordAuthor | Object detection | - |
dc.subject.keywordAuthor | Object detection | - |
dc.subject.keywordAuthor | Point cloud compression | - |
dc.subject.keywordAuthor | Three-dimensional displays | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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