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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪一平 | zh_TW |
| dc.contributor.advisor | Yi-Ping Hung | en |
| dc.contributor.author | 謝宜儒 | zh_TW |
| dc.contributor.author | I-Ju Hsieh | en |
| dc.date.accessioned | 2023-08-15T17:18:45Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-04 | - |
| dc.identifier.citation | H. Wang, S. Sridhar, J. Huang, J. Valentin, S. Song, and L. J. Guibas, “Normalized object coordinate space for category-level 6d object pose and size estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 2642–2651.
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Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014. L. N. Smith, “Cyclical learning rates for training neural networks,” in 2017 IEEE winter conference on applications of computer vision (WACV). IEEE, 2017, pp. 464–472. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88672 | - |
| dc.description.abstract | 類別層級之物體位姿估計致力於估測未見過之物體的六自由度位姿，而現有的方法大多倚賴像是物體位姿及 CAD 模型的標記。然而，在真實世界中以人工取得這些標記相當費時且容易出錯。因此，我們提出一種方法來解決類別層級之物體位姿估計中無監督領域自適應的問題。我們採用了一個師生共同學習的架構來同時利用有標記的合成資料與無標記的真實世界資料，學生模型與教師模型被訓練成在不同的干擾下做出一致的預測。此外，我們引入了領域對抗訓練來縮短合成資料與真實世界資料之間的差距，為了避免領域間錯誤的特徵對齊，我們使用了多個領域識別器，以在已知類別的情況下進行特徵對齊。實驗結果顯示我們的方法在公開的真實世界資料集上，取得了無監督方法中最好的結果。透過消融研究，我們也證明了我們的方法不受特定網路架構的限制，並可以作為一種於類別層級物體位姿估計的通用無監督領域自適應方法。 | zh_TW |
| dc.description.abstract | Category-level object pose estimation aims at predicting 6-DoF object poses for previously unseen objects. Current methods mostly rely on ground-truth labels such as object poses and CAD models. However, manually annotating these labels is time-consuming and error-prone in the real-world scenario. Hence, we propose a method to solve unsupervised domain adaptation (UDA) for category-level object pose estimation. We adopt a teacher-student joint learning framework to utilize both labeled synthetic data and unlabeled real-world data. The student model and the teacher model are trained to make consistent predictions under different perturbations. Furthermore, we introduce domain adversarial training to bridge the domain gap between synthetic and real-world data. To prevent false feature alignment between domains, we adopt multiple domain discriminators instead of a single one and perform category-aware alignments. Extensive experiments show that our method achieves state-of-the-art results among unsupervised methods on a public real-world dataset. Through ablation studies, we also demonstrate that our method is not restricted to certain network architectures and can serve as a general UDA method for category-level object pose estimation. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T17:18:45Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T17:18:45Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii List of Figures vi List of Tables viii 1 Introduction 1 2 Related Work 4 2.1 Category-Level Object Pose Estimation 4 2.1.1 Fully-Supervised Methods 4 2.1.2 Unsupervised Methods 6 2.2 Unsupervised Domain Adaptation 6 2.2.1 Feature Alignment 7 2.2.2 Consistency Regularization 7 2.2.3 Self-Training 7 3 Proposed Method 9 3.1 Problem Formulation 9 3.2 Overview 10 3.3 Deep Prior Deformation Network (DPDN) 11 3.4 Teacher and Student 12 3.4.1 Student Pre-training 12 3.4.2 Teacher-student Joint Learning 14 3.5 Domain Adversarial Training 15 3.6 Overall Training and Inference 18 4 Experiments 19 4.1 Datasets 19 4.2 Implementation Details 21 4.3 Evaluation Metrics 21 4.4 Quantitative Comparison with Existing Methods 22 4.5 Ablation Studies 23 4.5.1 Mean Teacher 25 4.5.2 Domain Adversarial Training 25 4.5.3 Unsupervised Loss 25 4.5.4 2D and 3D Augmentation 26 4.5.5 EMA Update 26 4.5.6 Base Network 27 4.6 Qualitative Results 28 5 Conclusion and Future Work 31 References 33 | - |
| dc.language.iso | en | - |
| dc.subject | 類別層級之物體位姿估計 | zh_TW |
| dc.subject | 平均教師 | zh_TW |
| dc.subject | 無監督領域自適應 | zh_TW |
| dc.subject | 領域對抗訓練 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | unsupervised domain adaptation | en |
| dc.subject | Mean Teacher | en |
| dc.subject | domain adversarial training | en |
| dc.subject | category-level object pose estimation | en |
| dc.subject | deep learning | en |
| dc.title | 域適應式平均教師於類別層級之物體位姿估計 | zh_TW |
| dc.title | Domain-Adaptive Mean Teacher for Category-Level Object Pose Estimation | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陳祝嵩;王鈺強;花凱龍;徐繼聖 | zh_TW |
| dc.contributor.oralexamcommittee | Chu-Song Chen;Yu-Chiang Wang;Kai-Lung Hua;Gee-Sern Hsu | en |
| dc.subject.keyword | 類別層級之物體位姿估計,無監督領域自適應,平均教師,領域對抗訓練,深度學習, | zh_TW |
| dc.subject.keyword | category-level object pose estimation,unsupervised domain adaptation,Mean Teacher,domain adversarial training,deep learning, | en |
| dc.relation.page | 41 | - |
| dc.identifier.doi | 10.6342/NTU202301994 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-08-08 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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