A Study on the Generality of a DNN-Based Monocular Depth Estimation
- Title
- A Study on the Generality of a DNN-Based Monocular Depth Estimation
- Author(s)
- Jinwoo Bae
- DGIST Authors
- Jinwoo Bae ; Sunghoon Im ; Kyong Hwan Jin
- Advisor
- 임성훈
- Co-Advisor(s)
- Kyong Hwan Jin
- Issued Date
- 2023
- Awarded Date
- 2023-02-01
- Type
- Thesis
- Description
- Monocular depth estimation, Generalization, Out-of-Distribution
- Table Of Contents
-
Ⅰ. Introduction 1
Ⅱ. Related Work 2
2.1 Self-supervised monocular depth estimation 2
2.2 Supervised monocular depth estimation 2
2.3 Vision Transformers 3
2.4 Modernized Architecture 4
Ⅲ. Method 4
3.1 CNN-Transformer Encoder 5
3.2 Attention Connection Module 6
3.3 Feature Fusion Decoder 7
Ⅳ. Experimental Results 7
4.1 Comparison on KITTI datasets 8
4.2 Analysis of texture-/shape-bias on CNN and Transformer 9
4.3 Analysis of Generalization performance 18
4.4 Analysis of feature representation on CNN and Transformer 21
Ⅴ. Conclusion 23
Ⅵ. References 25
Ⅶ. 요약문 31
- URI
-
http://hdl.handle.net/20.500.11750/45756
http://dgist.dcollection.net/common/orgView/200000653335
- Degree
- Master
- Publisher
- DGIST
- Related Researcher
-
-
Im, Sunghoon
- Research Interests Computer Vision; Deep Learning; Robot Vision
-
- Files in This Item:
-
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- Appears in Collections:
- Department of Electrical Engineering and Computer Science Theses Master
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