1
Unpaired Image-to-Image Translation with Shortest Path Regularization
2
CompletionFormer: Depth Completion with Convolutions and Vision Transformers
3
PatchCraft Self-Supervised Training for Correlated Image Denoising
4
Consistent Direct Time-of-Flight Video Depth Super-Resolution
5
Learning Complementary Correlations for Depth Super-Resolution With Incomplete Data in Real World
6
Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution
7
CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image
8
AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network
9
RGB-Depth Fusion GAN for Indoor Depth Completion
10
Multi-Sensor Large-Scale Dataset for Multi-View 3D Reconstruction
11
PDR-Net: Progressive depth reconstruction network for color guided depth map super-resolution
12
Fast, High-Quality Hierarchical Depth-Map Super-Resolution
13
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline
14
Deep edge map guided depth super resolution
15
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution
16
Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors
17
PMBANet: Progressive Multi-Branch Aggregation Network for Scene Depth Super-Resolution
18
Channel Attention Based Iterative Residual Learning for Depth Map Super-Resolution
19
Learning Texture Transformer Network for Image Super-Resolution
20
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting
21
Coupled Real-Synthetic Domain Adaptation for Real-World Deep Depth Enhancement
22
Structure-Preserving Super Resolution With Gradient Guidance
23
Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation
24
Unpaired Image Super-Resolution Using Pseudo-Supervision
25
Self-Supervised Deep Depth Denoising
26
StructureFlow: Image Inpainting via Structure-Aware Appearance Flow
27
U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
28
Feedback Network for Image Super-Resolution
29
Perceptual Deep Depth Super-Resolution
30
Photometric Depth Super-Resolution
31
Reconstruction-Based Pairwise Depth Dataset for Depth Image Enhancement Using CNN
32
InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset
33
Deep Learning for Single Image Super-Resolution: A Brief Review
34
T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks
35
To learn image super-resolution, use a GAN to learn how to do image degradation first
36
Toward Convolutional Blind Denoising of Real Photographs
37
Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks
38
Fight Ill-Posedness with Ill-Posedness: Single-shot Variational Depth Super-Resolution from Shading
39
Image Inpainting for Irregular Holes Using Partial Convolutions
40
Deep Depth Completion of a Single RGB-D Image
42
Deep Back-Projection Networks for Super-Resolution
43
Spectral Normalization for Generative Adversarial Networks
44
Generative Image Inpainting with Contextual Attention
45
Zero-Shot Super-Resolution Using Deep Internal Learning
46
Universal Denoising Networks : A Novel CNN Architecture for Image Denoising
47
Colored Point Cloud Registration Revisited
48
Matterport3D: Learning from RGB-D Data in Indoor Environments
49
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
50
Non-local Color Image Denoising with Convolutional Neural Networks
51
Least Squares Generative Adversarial Networks
52
Depth Map Super-Resolution by Deep Multi-Scale Guidance
53
Deep Joint Image Filtering
54
SyB3R: A Realistic Synthetic Benchmark for 3D Reconstruction from Images
55
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
56
A Deep Primal-Dual Network for Guided Depth Super-Resolution
57
ATGV-Net: Accurate Depth Super-Resolution
58
Instance Normalization: The Missing Ingredient for Fast Stylization
59
Deep Residual Learning for Image Recognition
60
SUN RGB-D: A RGB-D scene understanding benchmark suite
61
U-Net: Convolutional Networks for Biomedical Image Segmentation
62
Adam: A Method for Stochastic Optimization
63
A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM
64
High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth
65
Joint-adaptive bilateral depth map upsampling
66
Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation
67
A benchmark for surface reconstruction
68
Indoor Segmentation and Support Inference from RGBD Images
69
Understanding the difficulty of training deep feedforward neural networks
70
Joint bilateral upsampling
71
Colorization using optimization
72
Image quality assessment: from error visibility to structural similarity
73
‘‘Guideddepthsuper-resolutionbydeepanisotropicdiffusion
74
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
76
Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface re-integration
78
We split the dataset into training, validation and test sets, each using a different subset of scenes
79
We further split the training set into two disjoint parts, also using distinct scenes, Train A and Train B
80
The unpaired training set U includes low-quality images in Train A and high-quality images in Train B
81
Unpaired Translation Algorithm (Section 3.4)
82
Acknowledgements This work was supported by Ministry