9.8.1.1 Single View 3D Reconstruction, Convolutional Neural Networks, CNN

Chapter Contents (Back)
Single View. Monocular Depth. CNN. Convolutional Neural Networks.

Cao, Y., Wu, Z., Shen, C.,
Estimating Depth From Monocular Images as Classification Using Deep Fully Convolutional Residual Networks,
CirSysVideo(28), No. 11, November 2018, pp. 3174-3182.
IEEE DOI 1811
Estimation, Training, Semantics, Network architecture, Predictive models, Neural networks, Probability distribution, depth estimation BibRef

Amirkolaee, H.A.[Hamed Amini], Arefi, H.[Hossein],
Height estimation from single aerial images using a deep convolutional encoder-decoder network,
PandRS(149), 2019, pp. 50-66.
Elsevier DOI 1903
Convolutional neural network, Height image, Digital aerial image, Encoder, Decoder BibRef

Liu, J.W.[Ji-Wei], Zhang, Y.Z.[Yun-Zhou], Cui, J.H.[Jia-Hua], Feng, Y.H.[Yong-Hui], Pang, L.Z.[Lin-Zhuo],
Fully convolutional multi-scale dense networks for monocular depth estimation,
IET-CV(13), No. 5, August 2019, pp. 515-522.
DOI Link 1908
BibRef

Yang, X., Gao, Y., Luo, H., Liao, C., Cheng, K.,
Bayesian DeNet: Monocular Depth Prediction and Frame-Wise Fusion With Synchronized Uncertainty,
MultMed(21), No. 11, November 2019, pp. 2701-2713.
IEEE DOI 1911
Uncertainty, Cameras, Bayes methods, Simultaneous localization and mapping, Training, Video sequences, convolutional neural network BibRef

Chen, S.[Songnan], Tang, M.X.[Meng-Xia], Kan, J.[Jiangming],
Encoder-decoder with densely convolutional networks for monocular depth estimation,
JOSA-A(36), No. 10, October 2019, pp. 1709-1718.
DOI Link 1912
Feature extraction, Image registration, Image resolution, Motion estimation, Neural networks, Stochastic gradient descent BibRef

Yan, H., Yu, X., Zhang, Y., Zhang, S., Zhao, X., Zhang, L.,
Single Image Depth Estimation With Normal Guided Scale Invariant Deep Convolutional Fields,
CirSysVideo(29), No. 1, January 2019, pp. 80-92.
IEEE DOI 1901
Estimation, Semantics, Memory management, Feature extraction, multitask CNN BibRef

Padhy, R.P.[Ram Prasad], Chang, X.J.[Xiao-Jun], Choudhury, S.K.[Suman Kumar], Sa, P.K.[Pankaj Kumar], Bakshi, S.[Sambit],
Multi-stage cascaded deconvolution for depth map and surface normal prediction from single image,
PRL(127), 2019, pp. 165-173.
Elsevier DOI 1911
Scene understanding, Depth map, Surface normal, CNN, Multi-stage, Deconvolution BibRef

Song, W., Li, S., Liu, J., Hao, A., Zhao, Q., Qin, H.,
Contextualized CNN for Scene-Aware Depth Estimation From Single RGB Image,
MultMed(22), No. 5, May 2020, pp. 1220-1233.
IEEE DOI 2005
Estimation, Semantics, Training, Task analysis, Feature extraction, Decoding, Convolution, Depth Estimation, CNN, Single RGB Image, Scene-Aware Algorithm BibRef

Zhang, Y.Y.[Yu-Yang], Xu, S.B.[Shi-Biao], Wu, B.Y.[Bao-Yuan], Shi, J.[Jian], Meng, W.L.[Wei-Liang], Zhang, X.P.[Xiao-Peng],
Unsupervised Multi-View Constrained Convolutional Network for Accurate Depth Estimation,
IP(29), 2020, pp. 7019-7031.
IEEE DOI 2007
Estimation, Training, Feature extraction, Geometry, Computer vision, Cameras, Unsupervised learning, Unsupervised learning, depth consistency BibRef

Liu, J.[Jun], Li, Q.[Qing], Cao, R.[Rui], Tang, W.M.[Wen-Ming], Qiu, G.P.[Guo-Ping],
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation,
PandRS(166), 2020, pp. 255-267.
Elsevier DOI 2007
Monocular depth estimation, Convolutional neural network, Unsupervised learning, Lightweight, Real-time BibRef

Cheng, X.J.[Xin-Jing], Wang, P.[Peng], Yang, R.G.[Rui-Gang],
Learning Depth with Convolutional Spatial Propagation Network,
PAMI(42), No. 10, October 2020, pp. 2361-2379.
IEEE DOI 2009
BibRef
Earlier:
Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network,
ECCV18(XVI: 108-125).
Springer DOI 1810
Estimation, Task analysis, Cameras, Laser radar, Convolutional codes, Benchmark testing, spatial pyramid pooling BibRef

Chen, Z.[Zeyu], Wu, B.[Bo], Liu, W.C.[Wai Chung],
Mars3DNet: CNN-Based High-Resolution 3D Reconstruction of the Martian Surface from Single Images,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef


Sun, Y.H.[Yun-Han], Shi, J.L.[Jin-Long], Bai, S.[Suqin], Qian, Q.A.[Qi-Ang], Sun, Z.X.[Zheng-Xing],
Single View Depth Estimation via Dense Convolution Network with Self-supervision,
MMMod20(II:241-253).
Springer DOI 2003
BibRef

Rodríguez-Santiago, A.L.[Armando Levid], Arias-Aguilar, J.A.[José Anibal], Petrilli-Barceló, A.E.[Alberto Elías], Miranda-Luna, R.[Rosebet],
A Simple Methodology for 2d Reconstruction Using a CNN Model,
MCPR20(98-107).
Springer DOI 2007
BibRef

Fang, Z., Chen, X., Chen, Y., Van Gool, L.J.,
Towards Good Practice for CNN-Based Monocular Depth Estimation,
WACV20(1080-1089)
IEEE DOI 2006
Estimation, Training, Computer architecture, Decoding, Analytical models, Image resolution, Network architecture BibRef

Luis, J., Bello, G., Kim, M.,
A Novel Monocular Disparity Estimation Network with Domain Transformation and Ambiguity Learning,
ICIP19(474-478)
IEEE DOI 1910
Monocular disparity estimation, deep convolutional neural networks (DCNN), unsupervised learning BibRef

Kumari, S., Jha, R.R., Bhavsar, A., Nigam, A.,
AUTODEPTH: Single Image Depth Map Estimation via Residual CNN Encoder-Decoder and Stacked Hourglass,
ICIP19(340-344)
IEEE DOI 1910
Depth map estimation, CNN, Residual connection, Encoder-decoder, Hourglass BibRef

Shin, D., Ren, Z., Sudderth, E., Fowlkes, C.C.,
3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers,
ICCV19(2172-2182)
IEEE DOI 2004
cameras, computational geometry, convolutional neural nets, image colour analysis, image reconstruction, Surface reconstruction BibRef

Kaneko, M., Sakurada, K., Aizawa, K.,
TriDepth: Triangular Patch-Based Deep Depth Prediction,
DeepSLAM19(3747-3750)
IEEE DOI 2004
convolutional neural nets, feature extraction, image colour analysis, image reconstruction, single view depth prediction BibRef

Hu, J.J.[Jun-Jie], Zhang, Y.[Yan], Okatani, T.[Takayuki],
Visualization of Convolutional Neural Networks for Monocular Depth Estimation,
ICCV19(3868-3877)
IEEE DOI 2004
computer vision, convolutional neural nets, feature extraction, object detection, convolutional neural networks, Convolutional neural networks BibRef

Zhou, Y., Qi, H., Zhai, Y., Sun, Q., Chen, Z., Wei, L., Ma, Y.,
Learning to Reconstruct 3D Manhattan Wireframes From a Single Image,
ICCV19(7697-7706)
IEEE DOI 2004
computer vision, convolutional neural nets, image reconstruction, image representation, learning (artificial intelligence), Image reconstruction BibRef

Atapour-Abarghouei, A.[Amir], Breckon, T.P.[Toby P.],
To Complete or to Estimate, That is the Question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation,
3DV19(183-193)
IEEE DOI 1911
BibRef
And:
Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior,
ICIP19(4295-4299)
IEEE DOI 1910
BibRef
Earlier:
Real-Time Monocular Depth Estimation Using Synthetic Data with Domain Adaptation via Image Style Transfer,
CVPR18(2800-2810)
IEEE DOI 1812
Estimation, Training, Generators, Data models, Laser radar, Training data, Task analysis, Monocular Depth Estimation, 3D Scene Understanding. Adaptation models, Predictive models, Neural networks. Monocular Depth Estimation, Convolutional Neural Networks, Semantic Segmentation BibRef

Hu, J., Ozay, M., Zhang, Y., Okatani, T.,
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps With Accurate Object Boundaries,
WACV19(1043-1051)
IEEE DOI 1904
convolutional neural nets, feature extraction, image fusion, image reconstruction, image resolution, inference mechanisms, Image edge detection BibRef

Xu, D., Wang, W., Tang, H., Liu, H., Sebe, N., Ricci, E.,
Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation,
CVPR18(3917-3925)
IEEE DOI 1812
Estimation, Predictive models, Task analysis, Computer architecture, Semantics, Computational modeling, Fuses BibRef

Yang, F.T.[Feng-Ting], Zhou, Z.[Zihan],
Recovering 3D Planes from a Single Image via Convolutional Neural Networks,
ECCV18(X: 87-103).
Springer DOI 1810
BibRef

da Silveira, T.L.T., Dal'aqua, L.P., Jung, C.R.,
Indoor Depth Estimation from Single Spherical Images,
ICIP18(2935-2939)
IEEE DOI 1809
Estimation, Cameras, Distortion, Image color analysis, Training, Convolutional neural networks, Solid modeling, Spherical images, BibRef

Zhao, S.Y.[Shi-Yu], Zhang, L.[Lin], Shen, Y.[Ying], Zhu, Y.N.[Yong-Ning],
A CNN-Based Depth Estimation Approach with Multi-scale Sub-pixel Convolutions and a Smoothness Constraint,
ACCV18(II:365-380).
Springer DOI 1906
BibRef

Koch, T.[Tobias], Liebel, L.[Lukas], Fraundorfer, F.[Friedrich], Körner, M.[Marco],
Evaluation of CNN-Based Single-Image Depth Estimation Methods,
DeepLearn-G18(III:331-348).
Springer DOI 1905
BibRef

He, L., Yu, M., Wang, G.,
Spindle-Net: CNNs for Monocular Depth Inference with Dilation Kernel Method,
ICPR18(2504-2509)
IEEE DOI 1812
Convolution, Image resolution, Kernel, Feature extraction, Neural networks, Computer architecture, Task analysis BibRef

Jaritz, M., Charette, R.D., Wirbel, E., Perrotton, X., Nashashibi, F.,
Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation,
3DV18(52-60)
IEEE DOI 1812
computer vision, feature extraction, image colour analysis, image segmentation, learning (artificial intelligence), RGB+sparse depth fusion BibRef

Moukari, M., Picard, S., Simoni, L., Jurie, F.,
Deep Multi-Scale Architectures for Monocular Depth Estimation,
ICIP18(2940-2944)
IEEE DOI 1809
Training, Estimation, Decoding, Computer architecture, Semantics, Spatial resolution, Task analysis, monocular depth estimation, CNN architecture BibRef

Johnston, A., Garg, R., Carneiro, G., Reid, I.D.[Ian D.],
Scaling CNNs for High Resolution Volumetric Reconstruction from a Single Image,
DeepLearn-G17(930-939)
IEEE DOI 1802
Convolution, Deconvolution, Discrete cosine transforms, Image reconstruction, Shape, Solid modeling, Training BibRef

Hua, Y., Tian, H., Cai, A., Shi, P.,
Cross-modal correlation learning with deep convolutional architecture,
VCIP15(1-4)
IEEE DOI 1605
Analytical models BibRef

Tian, H.[Hu], Zhuang, B.[Bojin], Hua, Y.[Yan], Cai, A.[Anni],
Depth inference with convolutional neural network,
VCIP14(169-172)
IEEE DOI 1504
BibRef
Earlier:
Depth extraction from a single image by sampling based on distance metric learning,
ICIP14(2017-2021)
IEEE DOI 1502
feature extraction. Estimation. Mahalanobis distance rather than Euclidean distance between images. depth fusion. BibRef

Chapter on 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings continues in
Single View 3D Reconstruction, Generative Adversarial Networks, GAN .


Last update:Oct 20, 2021 at 09:45:26