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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 | 黃聖喻 | zh_TW |
| dc.contributor.author | Sheng-Yu Huang | en |
| dc.date.accessioned | 2026-02-03T16:18:19Z | - |
| dc.date.available | 2026-02-04 | - |
| dc.date.copyright | 2026-02-03 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2026-01-07 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101440 | - |
| dc.description.abstract | 近年來，3D電腦視覺的進展顯著提升了我們對複雜真實場景的理解能力。然而，真實世界的3D資料常因視角差異而變得不完整或存在跨視角的歧義，為穩健的3D建模帶來巨大挑戰。
本論文構建了一個統一的複雜3D視覺建模框架，研究分為物件層級與場景層級兩大任務。在物件層級中，首先解決了部分點雲的語義感知補全，以重建完整的幾何結構與語義標註；接著擴展單一模態生成至跨模態合成，將文字描述生成為3D模型。在場景層級中，我們示範了透過自然語言輸入精確定位3D場景中的目標物件，並在此基礎上執行場景級修補，移除指定物件後以多視角一致性技術填補缺失區域。這四大主題的系統整合顯著提升了建模的穩定性、生成品質、定位準確度與跨視角一致性，為沉浸式計算與機器人應用奠定了堅實基礎。 | zh_TW |
| dc.description.abstract | Recent advancements in 3D computer vision have significantly enhanced our capability to interpret complex real-world scenes. However, real-world 3D data often exhibit incompleteness and ambiguity across viewpoints, presenting considerable challenges for robust 3D modeling. This thesis focuses on constructing a unified framework for complex 3D visual modeling, organized into two main categories: Object-Level and Scene-Level. In the Object-Level sections, we first explore 3D Modeling via Semantic-Aware Completion, reconstructing 3D objects with complete geometry and semantic labels from partial point clouds and semantic information; then we examine 3D Modeling via Cross-Modal Generation, extending single-modal point cloud-to-point cloud transformations to text-driven 3D content synthesis. For Scene-Level sections, we start from introducing 3D Modeling via Scene-Level Visual Grounding, demonstrating how natural language prompts can precisely identify target objects within 3D scenes; finally, with the capability of locating any object in a given 3D scene, we present 3D Modeling via Scene-Level Inpainting, showcasing methods to remove specified objects and coherently fill missing regions using multi-view consistent techniques. Through the systematic integration of these four interconnected themes, our research demonstrates significant advancements in modeling robustness, generative fidelity, localization precision, and cross-view consistency, establishing a solid foundation for future applications in immersive computing, robotics, and beyond. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-02-03T16:18:19Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-02-03T16:18:19Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝i
中文摘要iii Abstract v Contents vii List of Figures xi List of Tables xix 1 3D Modeling via Semantic-Aware Completion 1 1.1 Publication Preface . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.1 Object completion . . . . . . . . . . . . . . . . . . . . . 4 1.3.2 Scene completion . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Proposed method . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4.1 Problem formulation and model overview . . . . . . . . . 6 1.4.2 Semantic-Prototype Variational Transformer . . . . . . . 7 1.4.3 Training and Inference . . . . . . . . . . . . . . . . . . . 11 1.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.5.1 Dataset and Implementation Details . . . . . . . . . . . . 12 1.5.2 Semantic Point Cloud Completion . . . . . . . . . . . . . 13 1.5.3 Further Analysis and Ablation Study . . . . . . . . . . . . 14 1.6 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2 3D Modeling via Cross-Modal Generation 21 2.1 Publication Preface . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.1 Text-guided 3D object generation . . . . . . . . . . . . . 25 2.3.2 Diffusion models . . . . . . . . . . . . . . . . . . . . . . 26 2.4 Proposed method . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4.1 Problem Formulation and Model Overview . . . . . . . . 27 2.4.2 Phrase Disentanglement using LLM . . . . . . . . . . . . 29 2.4.3 Learning Shape-Color Diffusion Priors . . . . . . . . . . 30 2.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.5.1 Dataset and Implementation Details . . . . . . . . . . . . 32 2.5.2 Text-guided 3D object generation . . . . . . . . . . . . . 33 2.5.3 Extensional experiments on visual-guided 3D generation. . 36 2.5.4 Further analysis . . . . . . . . . . . . . . . . . . . . . . . 37 2.6 Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3 3D Modeling via Scene-Level Visual Grounding 49 3.1 Publication Preface . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.1 2D Visual Grounding . . . . . . . . . . . . . . . . . . . . 53 3.3.2 3D Visual Grounding . . . . . . . . . . . . . . . . . . . . 54 3.3.3 Data-Efficient 3D Visual Grounding . . . . . . . . . . . . 55 3.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.4.1 Problem Formulation and Model Overview . . . . . . . . 57 3.4.2 3D Visual Grounding with Order-Aware Object Referring 58 3.4.3 Order-Aware Warm-up with Synthetic Referential Order . 61 3.4.4 Overall Training Pipeline . . . . . . . . . . . . . . . . . . 63 3.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.5.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.5.2 Quantitative Results for Data Efficiency . . . . . . . . . . 64 3.5.3 Ablation Studies . . . . . . . . . . . . . . . . . . . . . . 66 3.5.4 Qualitative Results . . . . . . . . . . . . . . . . . . . . . 67 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4 3D Modeling via Scene-Level Inpainting 69 4.1 Publication Preface . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.3 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.3.1 3D Representations for Novel View Synthesis . . . . . . . 73 4.3.2 3D Scene Inpainting . . . . . . . . . . . . . . . . . . . . 74 4.4 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.4.1 Problem Definition and Model Overview . . . . . . . . . 75 4.4.2 Inferring Depth-Guided Inpainting Masks . . . . . . . . . 77 4.4.3 Inpainting-guided 3DGS Refinement . . . . . . . . . . . 78 4.4.4 Training and Inference . . . . . . . . . . . . . . . . . . . 80 4.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.5.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.5.2 Quantitative Evaluations . . . . . . . . . . . . . . . . . . 83 4.5.3 Qualitative Results . . . . . . . . . . . . . . . . . . . . . 84 4.5.4 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . 85 4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5 Conclusion 87 Reference 89 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 深度學習 | - |
| dc.subject | 電腦視覺 | - |
| dc.subject | 3D 電腦視覺 | - |
| dc.subject | 點雲 | - |
| dc.subject | 3D生成 | - |
| dc.subject | 3D視覺定位3D高斯潑濺 | - |
| dc.subject | 場景修補 | - |
| dc.subject | deep learning | - |
| dc.subject | computer vision | - |
| dc.subject | 3D computer vision | - |
| dc.subject | point cloud | - |
| dc.subject | 3D generation | - |
| dc.subject | 3D visual grounding | - |
| dc.subject | Gaussian Splatting | - |
| dc.subject | 3D scene inpainting | - |
| dc.title | 邁向複雜3D視覺建構： 從物件層級補全與生成，到場景層級跨模態定位與修復 | zh_TW |
| dc.title | Towards Complex 3D Visual Modeling: From Object-Level Completion and Generation to Scene-Level Cross-Modal Localization and Inpainting | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 陳祝嵩;陳煥宗;莊永裕;劉育綸;孫誠 | zh_TW |
| dc.contributor.oralexamcommittee | Chu-Song Chen;Hwann-Tzong Chen;Yung-Yu Chuang;Yu-Lun Liu;Cheng Sun | en |
| dc.subject.keyword | 深度學習,電腦視覺3D 電腦視覺點雲3D生成3D視覺定位3D高斯潑濺場景修補 | zh_TW |
| dc.subject.keyword | deep learning,computer vision3D computer visionpoint cloud3D generation3D visual groundingGaussian Splatting3D scene inpainting | en |
| dc.relation.page | 115 | - |
| dc.identifier.doi | 10.6342/NTU202600037 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2026-01-08 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| dc.date.embargo-lift | 2026-02-04 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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