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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王鈺強 | zh_TW |
| dc.contributor.advisor | Yu-Chiang Frank Wang | en |
| dc.contributor.author | 紀彥仰 | zh_TW |
| dc.contributor.author | Yan-Yang Ji | en |
| dc.date.accessioned | 2023-08-15T16:52:40Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-28 | - |
| dc.identifier.citation | S. Zakharov, W. Kehl, A. Bhargava, and A. Gaidon, “Autolabeling 3d objects with differentiable rendering of sdf shape priors,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. vi, 1, 2, 6, 7, 17, 18, 19, 20
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Xia, “Multi-view 3d object detection network for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 16 A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., “Pytorch: An imperative style, high-performance deep learning library,” 2019. 16 C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 16 D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014. 17 T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in Proceedings of the International Conference on Machine Learning (ICML), 2020. 21, 22 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88569 | - |
| dc.description.abstract | 三維物體偵測是三維視覺的一個熱門研究領域，近年來受到廣泛關注。然而，訓練用於三維物體偵測的深度學習模型通常需要大量帶有三維邊界框註釋的數據，這是一項耗時的任務並且存在重大挑戰。為了應對這一挑戰，我們提出了一種通過可變形模板匹配（DTMNet）進行弱監督三維物體偵測的方法，該方法在圖像和二維實例遮罩的弱監督下，通過將可變形形狀模板與輸入的LiDAR點雲進行匹配，生成弱監督的三維虛擬邊界框。生成的三維虛擬邊界框可以用於訓練基於圖像或基於LiDAR的三維物體偵測器。我們的DTMNet顯著降低了註釋成本，提高了三維物體偵測的效率。對KITTI基準數據集的實驗結果在定量和定性上證明了我們提出的模型的有效性和實用性 | zh_TW |
| dc.description.abstract | 3D object detection is an active research topic for 3D vision and has been widely studied in recent years. However, training deep learning models for 3D object detection typically requires extensive data with 3D bounding box annotations, which is a time-consuming task and presents a significant challenge. To address this challenge, we propose a weakly supervised 3D object detection method via deformable template matching (DTMNet), which generates weakly supervised 3D pseudo-bounding boxes by matching a deformable shape template with the input LiDAR point clouds under the weak supervision of images and 2D instance masks. The generated 3D pseudo-bounding boxes can be used to train either image-based or LiDAR-based 3D object detectors. Our DTMNet significantly reduces annotation costs and improves the efficiency of 3D object detection. Experimental results on the KITTI benchmark dataset quantitatively and qualitatively demonstrate the effectiveness and practicality of our proposed model. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T16:52:40Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T16:52:40Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 中文摘要 i
Abstract ii List of Figures v List of Tables vii 1 Introduction 1 2 Related Work 4 2.1 Supervised 3D object detection 4 2.2 Semi-supervised 3D object detection 5 2.3 Weakly supervised 3D object detection 6 3 Proposed Method 8 3.1 Problem formulation and model overview 8 3.2 Weakly supervised deformable template matching 10 3.2.1 Segmentor and Predictor 10 3.2.2 Edge and color supervision 12 3.3 Training and obtaining pseudo-bounding box 15 4 Experiments 16 4.1 Dataset and implementation details 16 4.1.1 Dataset 16 4.1.2 Implementation Details 16 4.2 Weakly supervised 3D object detection 17 4.2.1 Quantitative evaluation 17 4.2.2 Qualitative result 18 4.3 Ablation Study 20 4.4 Additional Experiment Results 22 5 Conclusion 25 Reference 26 | - |
| dc.language.iso | en | - |
| dc.title | 基於可變形模板匹配之弱監督三維物體檢測 | zh_TW |
| dc.title | Weakly Supervised 3D Object Detection via Deformable Template Matching | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 孫紹華;陳祝嵩 | zh_TW |
| dc.contributor.oralexamcommittee | Shao-Hua Sun;Chu-Song Chen | en |
| dc.subject.keyword | 物體偵測,三維視覺,點雲, | zh_TW |
| dc.subject.keyword | Object Detection,3D Vision,Point Cloud, | en |
| dc.relation.page | 29 | - |
| dc.identifier.doi | 10.6342/NTU202302077 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-01 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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