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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊永裕(Yung-Yu Chuang) | |
| dc.contributor.author | Ching-Ya Chiu | en |
| dc.contributor.author | 邱靖雅 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:12:13Z | - |
| dc.date.available | 2022-01-01 | |
| dc.date.available | 2022-11-24T03:12:13Z | - |
| dc.date.copyright | 2021-11-04 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-21 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80664 | - |
| dc.description.abstract | 多視角立體視覺的目標是透過多張影像以及相對應的相機參數，還原場景的三維資訊。近年來，隨著深度學習的發展，許多論文在多視角立體視覺的題目上取得優異的成果。然而，在還原大型場景時，現有的方法會需要較多的勞力以確保取得的影像之間有足夠的重疊。因此，我們提出一個新的想法，使用全景影像作為多視角立體視覺的輸入以推斷場景的三維幾何資訊。全景圖的優點在於它們能夠獲得完整的環境訊息，並在單張影像中提供較廣泛且連續的資訊。為此，我們提出360MVSNet，一個用於360°影像的多視角立體視覺深度學習模型。為了使訓練的過程能夠考量到360°相機提供的幾何資訊，我們提出球型掃描的方法，根據所假設的深度將影像特徵投影到不同半徑的球體上做計算。透過多尺度的立體成本容積以及測量每個尺度模型的不確定性，我們能夠階段性的預測影像的深度，並生成高解析度的深度圖。除此之外，我們建立一個大型的合成資料集EQMVS，它包含50000張左右的RGB影像、深度圖以及相機參數。透過實驗結果證明，我們的模型在測試資料集以及真實世界的場景都能較完整的還原整個場景，同時在數據上超越其他的方法。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:12:13Z (GMT). No. of bitstreams: 1 U0001-2010202100242300.pdf: 3884653 bytes, checksum: 2b7a8bc111a9d8f020424f230473b116 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i Acknowledgements iii 摘要 v Abstract vii Contents ix List of Figures xi List of Tables xiii Denotation xv Chapter 1 Introduction 1 1.1 Introduction 1 Chapter 2 Related Work 5 2.1 Tradition Multiview Stereo 5 2.2 Deep Learning Multiview Stereo 6 2.3 Omnidirectional Depth Estimation and Stereo Matching 7 Chapter 3 Spherical Multiview Stereo 9 3.1 Feature Extraction 9 3.2 Spherical Sweeping 11 3.3 Multi Scale Cost Volume 17 3.4 Depth Regression and Loss Function 19 Chapter 4 Synthetic Dataset: EQMVS 21 4.1 Data Acquisition 21 Chapter 5 Experiments and Results 25 5.1 Implementation Details 25 5.2 Performance 26 5.3 Ablation Study 29 Chapter 6 Conclusion 33 References 35 | |
| dc.language.iso | en | |
| dc.subject | 電腦視覺 | zh_TW |
| dc.subject | 多視角立體視覺 | zh_TW |
| dc.subject | 360度影像 | zh_TW |
| dc.subject | 三維場景重建 | zh_TW |
| dc.subject | 全景影像 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | Equirectangular Image | en |
| dc.subject | Computer Vision | en |
| dc.subject | Deep Learning | en |
| dc.subject | Multi-View Stereo | en |
| dc.subject | 3D Scene Reconstruction | en |
| dc.subject | 360° Image | en |
| dc.title | 360MVSNet:基於360°影像之多視角立體視覺深度模型 | zh_TW |
| dc.title | 360MVSNet: Deep Multi-View Stereo Network with 360° Images | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 朱宏國(Hsin-Tsai Liu),李明穗(Chih-Yang Tseng) | |
| dc.subject.keyword | 多視角立體視覺,360度影像,三維場景重建,全景影像,深度學習,電腦視覺, | zh_TW |
| dc.subject.keyword | Multi-View Stereo,360° Image,3D Scene Reconstruction,Equirectangular Image,Deep Learning,Computer Vision, | en |
| dc.relation.page | 39 | |
| dc.identifier.doi | 10.6342/NTU202103906 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-22 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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