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

He, L., Wang, G., Hu, Z.,
Learning Depth From Single Images With Deep Neural Network Embedding Focal Length,
IP(27), No. 9, September 2018, pp. 4676-4689.
IEEE DOI 1807
Markov processes, image processing, learning (artificial intelligence), neural nets, single images BibRef

Zhang, Z.Y.[Zhen-Yu], Xu, C.Y.[Chun-Yan], Yang, J.[Jian], Tai, Y.[Ying], Chen, L.[Liang],
Deep hierarchical guidance and regularization learning for end-to-end depth estimation,
PR(83), 2018, pp. 430-442.
Elsevier DOI 1808
Depth estimation, Multi-regularization, Deep neural network 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.M.[Jiang-Ming],
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

Chen, S.[Songnan], Han, J.Y.[Jun-Yu], Tang, M.X.[Meng-Xia], Dong, R.[Ruifang], Kan, J.M.[Jiang-Ming],
Encoder-Decoder Structure with Multiscale Receptive Field Block for Unsupervised Depth Estimation from Monocular Video,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
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

Li, J.[Jun], Yuce, C.[Can], Klein, R.[Reinhard], Yao, A.[Angela],
A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images,
CVIU(186), 2019, pp. 25-36.
Elsevier DOI 1908
BibRef
Earlier: A1, A3, A4, Only: ICCV17(3392-3400)
IEEE DOI 1802
Depth estimation, Depth gradient, Set loss, Indoor scenes, Man-made objects. image colour analysis, learning (artificial intelligence), NYU, NYU Depth, accurate depth map, deep learning methods, Training 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, 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

Liu, J.[Jun], Li, Q.[Qing], Cao, R.[Rui], Tang, W.M.[Wen-Ming], Qiu, G.P.[Guo-Ping],
A contextual conditional random field network for monocular depth estimation,
IVC(98), 2020, pp. 103922.
Elsevier DOI 2006
Monocular depth estimation, Deep neural network, Skip connection, Conditional random field BibRef

Cao, Y.Z.[Yuan-Zhouhan], Zhao, T.Q.[Tian-Qi], Xian, K.[Ke], Shen, C.H.[Chun-Hua], Cao, Z.G.[Zhi-Guo], Xu, S.G.[Shu-Gong],
Monocular Depth Estimation With Augmented Ordinal Depth Relationships,
CirSysVideo(30), No. 8, August 2020, pp. 2674-2682.
IEEE DOI 2008
Estimation, Measurement, Videos, Training, Motion pictures, Task analysis, Labeling, Depth estimation, RGB-D dataset, deep network BibRef

Ali, U.[Usman], Bayramli, B.[Bayram], Alsarhan, T.[Tamam], Lu, H.T.[Hong-Tao],
A lightweight network for monocular depth estimation with decoupled body and edge supervision,
IVC(113), 2021, pp. 104261.
Elsevier DOI 2108
Monocular depth estimation, Deep learning, Lightweight network BibRef

Tao, Y.[Yu], Muller, J.P.[Jan-Peter], Xiong, S.[Siting], Conway, S.J.[Susan J.],
MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Chang, Y.[Yakun], Jung, C.[Cheolkon], Sun, J.[Jun],
Joint Reflection Removal and Depth Estimation from a Single Image,
Cyber(51), No. 12, December 2021, pp. 5836-5849.
IEEE DOI 2112
Estimation, Glass, Cameras, Image edge detection, Feature extraction, Collaboration, Deep learning, transmission recovery BibRef

Huynh, L.[Lam], Pedone, M.[Matteo], Nguyen, P.[Phong], Matas, J.[Jiri], Rahtu, E.[Esa], Heikkilä, J.[Janne],
Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Loss,
3DV21(228-238)
IEEE DOI 2201
Image sensors, Deep learning, Neural networks, Estimation, Sensors, saliency detection, self-attention, normalized Hessian loss 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

Zhao, J.S.[Jiang-San], Kumar, A.[Ajay], Banoth, B.N.[Balaji Naik], Marathi, B.[Balram], Rajalakshmi, P.[Pachamuthu], Rewald, B.[Boris], Ninomiya, S.[Seishi], Guo, W.[Wei],
Deep-Learning-Based Multispectral Image Reconstruction from Single Natural Color RGB Image: Enhancing UAV-Based Phenotyping,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Nie, W.Z.[Wei-Zhi], Liu, A.A.[An-An], Zhao, S.C.[Si-Cheng], Gao, Y.[Yue],
Deep Correlated Joint Network for 2-D Image-Based 3-D Model Retrieval,
Cyber(52), No. 3, March 2022, pp. 1862-1871.
IEEE DOI 2203
Solid modeling, Shape, Feature extraction, Visualization, Correlation, Loss measurement, Benchmark testing, deep metric learning BibRef

Wu, B.Y.[Bing-Yuan], Wang, Y.X.[Yong-Xiong],
Rich global feature guided network for monocular depth estimation,
IVC(125), 2022, pp. 104520.
Elsevier DOI 2208
Monocular depth estimation, Transformer, Large kernel convolution attention, Global feature BibRef


Wang, L.J.[Li-Jun], Wang, Y.[Yifan], Wang, L.[Linzhao], Zhan, Y.L.[Yun-Long], Wang, Y.[Ying], Lu, H.C.[Hu-Chuan],
Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner?,
ICCV21(12707-12716)
IEEE DOI 2203
Point cloud compression, Art, Motion estimation, Design methodology, Estimation, BibRef

Huynh, L.[Lam], Nguyen, P.[Phong], Matas, J.[Jirí], Rahtu, E.[Esa], Heikkilä, J.[Janne],
Lightweight Monocular Depth with a Novel Neural Architecture Search Method,
WACV22(326-336)
IEEE DOI 2202
Search methods, Computational modeling, Estimation, Computer architecture, Manuals, Benchmark testing, 3D Computer Vision Deep Learning BibRef

Park, D.[Dennis], Ambrus, R.[Rares], Guizilini, V.[Vitor], Li, J.[Jie], Gaidon, A.[Adrien],
Is Pseudo-Lidar needed for Monocular 3D Object detection?,
ICCV21(3122-3132)
IEEE DOI 2203
Training, Estimation, Detectors, Object detection, Manuals, Benchmark testing, Detection and localization in 2D and 3D, Vision for robotics and autonomous vehicles BibRef

Julca-Aguilar, F.[Frank], Taylor, J.[Jason], Bijelic, M.[Mario], Mannan, F.[Fahim], Tseng, E.[Ethan], Heide, F.[Felix],
Gated3D: Monocular 3D Object Detection From Temporal Illumination Cues,
ICCV21(2918-2928)
IEEE DOI 2203
Image segmentation, Laser radar, Lighting, Object detection, Logic gates, Feature extraction, Vision for robotics and autonomous vehicles BibRef

Lu, Y.[Yan], Ma, X.Z.[Xin-Zhu], Yang, L.[Lei], Zhang, T.Z.[Tian-Zhu], Liu, Y.[Yating], Chu, Q.[Qi], Yan, J.J.[Jun-Jie], Ouyang, W.L.[Wan-Li],
Geometry Uncertainty Projection Network for Monocular 3D Object Detection,
ICCV21(3091-3101)
IEEE DOI 2203
Training, Geometry, Solid modeling, Uncertainty, Computational modeling, Object detection, Vision applications and systems BibRef

Simonelli, A.[Andrea], Bulň, S.R.[Samuel Rota], Porzi, L.[Lorenzo], Kontschieder, P.[Peter], Ricci, E.[Elisa],
Are we Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection?,
ICCV21(3205-3213)
IEEE DOI 2203
Training, Estimation, Deep architecture, Object detection, Benchmark testing, Detection and localization in 2D and 3D, 3D from a single image and shape-from-x BibRef

Tilmon, B.[Brevin], Koppal, S.J.[Sanjeev J.],
SaccadeCam: Adaptive Visual Attention for Monocular Depth Sensing,
ICCV21(5989-5998)
IEEE DOI 2203
Visualization, Image resolution, Adaptive systems, Animals, Prototypes, Estimation, Stereo, Computational photography BibRef

Xiong, J.H.[Jin-Hui], Heidrich, W.[Wolfgang],
In-the-Wild Single Camera 3D Reconstruction Through Moving Water Surfaces,
ICCV21(12538-12547)
IEEE DOI 2203
Geometry, Surface reconstruction, Casting, Shape, Laboratories, 3D from a single image and shape-from-x, Stereo, 3D from multiview and other sensors BibRef

Jung, H.[Hyunyoung], Park, E.[Eunhyeok], Yoo, S.[Sungjoo],
Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation,
ICCV21(12622-12632)
IEEE DOI 2203
Training, Measurement, Geometry, Codes, Semantics, Estimation, 3D from a single image and shape-from-x, Vision for robotics and autonomous vehicles BibRef

Liu, L.[Lina], Song, X.[Xibin], Wang, M.M.[Meng-Meng], Liu, Y.[Yong], Zhang, L.J.[Liang-Jun],
Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation,
ICCV21(12717-12726)
IEEE DOI 2203
Codes, Estimation, Lighting, Generative adversarial networks, Feature extraction, Data mining, 3D from multiview and other sensors BibRef

Liu, Z.D.[Zong-Dai], Zhou, D.F.[Ding-Fu], Lu, F.X.[Fei-Xiang], Fang, J.[Jin], Zhang, L.J.[Liang-Jun],
AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection,
ICCV21(15621-15630)
IEEE DOI 2203
Deformable models, Deep learning, Solid modeling, Shape, Neural networks, Pipelines, Detection and localization in 2D and 3D BibRef

Wang, T.[Tai], Zhu, X.G.[Xin-Ge], Pang, J.M.[Jiang-Miao], Lin, D.[Dahua],
FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection,
3DODI21(913-922)
IEEE DOI 2112
Convolutional codes, Training, Costs, Estimation, Object detection, Transforms BibRef

Huynh, L.[Lam], Nguyen, P.[Phong], Matas, J.[Jirí], Rahtu, E.[Esa], Heikkilä, J.[Janne],
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion,
ICCV21(12747-12756)
IEEE DOI 2203
Point cloud compression, Deep learning, Solid modeling, Fuses, Computational modeling, BibRef

Wei, J., Jiang, J., Yilmaz, A.,
MOHE-Net: Monocular Object Height Estimation Network Using Deep Learning and Scene Geometry,
ISPRS21(B2-2021: 557-564).
DOI Link 2201
BibRef

Lo, C.C.[Chen-Chou], Vandewalle, P.[Patrick],
Depth Estimation from Monocular Images and Sparse Radar Using Deep Ordinal Regression Network,
ICIP21(3343-3347)
IEEE DOI 2201
Deep learning, Rain, Radar measurements, Image processing, Estimation, Radar, monocular depth estimation, radar, nuScenes BibRef

Li, R.[Runfa], Nguyen, T.[Truong],
SM3D: Simultaneous Monocular Mapping and 3D Detection,
ICIP21(3652-3656)
IEEE DOI 2201
Training, Location awareness, Deep learning, Image processing, Pose estimation, SM3D, Monocular Mapping, Monocular 3D detection, Depth Estimation BibRef

Roddick, T.[Thomas], Biggs, B.[Benjamin], Reino, D.O.[Daniel Olmeda], Cipolla, R.[Roberto],
On the Road to Large-Scale 3D Monocular Scene Reconstruction using Deep Implicit Functions,
AVVision21(2875-2884)
IEEE DOI 2112
Training, Image resolution, Shape, Roads, Pipelines, Hazards BibRef

Hornauer, J.[Julia], Nalpantidis, L.[Lazaros], Belagiannis, V.[Vasileios],
Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained Hardware,
ERCVAD21(954-962)
IEEE DOI 2112
Training, Deep learning, Visualization, Adaptation models, Neural networks, Estimation, Computer architecture BibRef

Pintore, G.[Giovanni], Agus, M.[Marco], Almansa, E.[Eva], Schneider, J.[Jens], Gobbetti, E.[Enrico],
SliceNet: deep dense depth estimation from a single indoor panorama using a slice-based representation,
CVPR21(11531-11540)
IEEE DOI 2111
Deep learning, Surface reconstruction, Estimation, Feature extraction, Indoor environment BibRef

Bae, K.[Kyuho], Ivan, A.[Andre], Nagahara, H.[Hajime], Park, I.K.[In Kyu],
5D Light Field Synthesis from a Monocular Video,
ICPR21(7157-7164)
IEEE DOI 2105
Learning systems, Deep learning, Feature extraction, Cameras, Light fields, Image reconstruction BibRef

Hidalgo-Carrió, J., Gehrig, D., Scaramuzza, D.,
Learning Monocular Dense Depth from Events,
3DV20(534-542)
IEEE DOI 2102
Cameras, Estimation, Sensors, Standards, Measurement, Robot vision systems, Training, deep learning, depth BibRef

Chen, Z., Wu, B., Liu, W.C.,
Deep Learning for 3d Reconstruction of the Martian Surface Using Monocular Images: A First Glance,
ISPRS20(B3:1111-1116).
DOI Link 2012
BibRef

Aleotti, F.[Filippo], Tosi, F.[Fabio], Zhang, L.[Li], Poggi, M.[Matteo], Mattoccia, S.[Stefano],
Reversing the Cycle: Self-supervised Deep Stereo Through Enhanced Monocular Distillation,
ECCV20(XI:614-632).
Springer DOI 2011
BibRef

Swami, K., Bondada, P.V., Bajpai, P.K.,
ACED: Accurate And Edge-Consistent Monocular Depth Estimation,
ICIP20(1376-1380)
IEEE DOI 2011
Estimation, Training, Computational modeling, Convolution, Task analysis, Cameras, Machine learning, deep learning BibRef

Xu, Y.F.[Yi-Fan], Fan, T.Q.[Tian-Qi], Yuan, Y.[Yi], Singh, G.[Gurprit],
Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3d Reconstruction with Symmetry,
ECCV20(I:248-263).
Springer DOI 2011
BibRef

Ren, H., Raj, A., El-Khamy, M., Lee, J.,
SUW-Learn: Joint Supervised, Unsupervised, Weakly Supervised Deep Learning for Monocular Depth Estimation,
DeepVision20(3235-3243)
IEEE DOI 2008
Estimation, Supervised learning, Training, Unsupervised learning, Semantics BibRef

Ren, H., El-Khamy, M., Lee, J.,
Stereo Disparity Estimation via Joint Supervised, Unsupervised, and Weakly Supervised Learning,
ICIP20(2760-2764)
IEEE DOI 2011
Supervised learning, Estimation, Feature extraction, Unsupervised learning, Loss measurement, Error analysis, Training, weakly supervised learning BibRef

Smirnov, D., Fisher, M., Kim, V.G., Zhang, R., Solomon, J.,
Deep Parametric Shape Predictions Using Distance Fields,
CVPR20(558-567)
IEEE DOI 2008
Shape, Task analysis, Geometry, Loss measurement, Machine learning BibRef

Xu, S., Yang, J., Chen, D., Wen, F., Deng, Y., Jia, Y., Tong, X.,
Deep 3D Portrait From a Single Image,
CVPR20(7707-7717)
IEEE DOI 2008
Face, Geometry, Image reconstruction, Hair 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
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
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

Gadelha, M., Wang, R., Maji, S.,
Shape Reconstruction Using Differentiable Projections and Deep Priors,
ICCV19(22-30)
IEEE DOI 2004
gradient methods, image reconstruction, noisy projections, viewpoint uncertainities, shape given measurements, Bayes methods BibRef

Chang, J., Wetzstein, G.,
Deep Optics for Monocular Depth Estimation and 3D Object Detection,
ICCV19(10192-10201)
IEEE DOI 2004
image capture, image coding, neural nets, object detection, optimisation, stereo image processing, deep optics, Object detection BibRef

Ramon, E., Ruiz, G., Batard, T., Giró-i-Nieto, X.,
Hyperparameter-Free Losses for Model-Based Monocular Reconstruction,
GMDL19(4075-4084)
IEEE DOI 2004
cameras, computational complexity, computational geometry, image reconstruction, minimisation, pose estimation, deep learning BibRef

van Dijk, T., de Croon, G.,
How Do Neural Networks See Depth in Single Images?,
ICCV19(2183-2191)
IEEE DOI 2004
cameras, image processing, neural nets, deep neural networks, depth estimation, camera pitch, vertical image position, Cameras, Biological neural networks BibRef

Liu, L.J.[Li-Jie], Lu, J.W.[Ji-Wen], Xu, C.J.[Chun-Jing], Tian, Q.[Qi], Zhou, J.[Jie],
Deep Fitting Degree Scoring Network for Monocular 3D Object Detection,
CVPR19(1057-1066).
IEEE DOI 2002
BibRef

Hsieh, Y., Lin, W., Li, D., Chuang, J.,
Deep Learning-Based Obstacle Detection and Depth Estimation,
ICIP19(1635-1639)
IEEE DOI 1910
Deep learning, YOLOv3, object detection, depth prediction, KITTI dataset BibRef

Irie, G., Kawanishi, T., Kashino, K.,
Robust Learning for Deep Monocular Depth Estimation,
ICIP19(964-968)
IEEE DOI 1910
Monocular depth estimation, robust loss function, supervised learning BibRef

Choi, S., Nguyen, A., Kim, J., Ahn, S., Lee, S.,
Point Cloud Deformation for Single Image 3d Reconstruction,
ICIP19(2379-2383)
IEEE DOI 1910
3D reconstruction, point cloud processing, neural network, deep learning BibRef

Li, R.[Ruibo], Xian, K.[Ke], Shen, C.H.[Chun-Hua], Cao, Z.G.[Zhi-Guo], Lu, H.[Hao], Hang, L.X.[Ling-Xiao],
Deep Attention-Based Classification Network for Robust Depth Prediction,
ACCV18(IV:663-678).
Springer DOI 1906
BibRef

Smith, R.[Rory], Burghardt, T.[Tilo],
DeepKey: Towards End-to-End Physical Key Replication from a Single Photograph,
GCPR18(487-502).
Springer DOI 1905
RGB image of a key, generate the 3D key. BibRef

Ito, S.[Seiya], Kaneko, N.[Naoshi], Shinohara, Y.[Yuma], Sumi, K.[Kazuhiko],
Deep Modular Network Architecture for Depth Estimation from Single Indoor Images,
3D-Wild18(I:324-336).
Springer DOI 1905
BibRef

Fu, H., Gong, M., Wang, C., Batmanghelich, K., Tao, D.,
Deep Ordinal Regression Network for Monocular Depth Estimation,
CVPR18(2002-2011)
IEEE DOI 1812
Estimation, Feature extraction, Training, Spatial resolution, Kernel BibRef

Kumar, A.C., Bhandarkar, S.M., Prasad, M.,
DepthNet: A Recurrent Neural Network Architecture for Monocular Depth Prediction,
DeepSLAM18(396-3968)
IEEE DOI 1812
Simultaneous localization and mapping, Image reconstruction, Recurrent neural networks, Video sequences BibRef

Ron, D., Duan, K., Ma, C., Xu, N., Wang, S., Hanumante, S., Sagar, D.,
Monocular Depth Estimation via Deep Structured Models with Ordinal Constraints,
3DV18(570-577)
IEEE DOI 1812
feedforward neural nets, image resolution, inference mechanisms, user interfaces, deep structured model, ordinal constraints BibRef

Carvalho, M., Saux, B.L., Trouvé-Peloux, P., Almansa, A., Champagnat, F.,
On Regression Losses for Deep Depth Estimation,
ICIP18(2915-2919)
IEEE DOI 1809
Estimation, Training, Standards, Convolution, Machine learning, Network architecture, Depth estimation, loss function BibRef

Huang, J.[Jun], Bi, T.T.[Tian-Teng], Liu, Y.[Yue], Wang, Y.T.[Yong-Tian],
Stereo Generation from a Single Image Using Deep Residual Network,
ICIP18(3653-3657)
IEEE DOI 1809
Painting, Training, Interpolation, Measurement, Stereo image processing, Image edge detection, layered images BibRef

Yang, N.[Nan], Wang, R.[Rui], Stückler, J.[Jörg], Cremers, D.[Daniel],
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry,
ECCV18(VIII: 835-852).
Springer DOI 1810
BibRef

Kuznietsov, Y., Stückler, J.[Jörg], Leibe, B.[Bastian],
Semi-Supervised Deep Learning for Monocular Depth Map Prediction,
CVPR17(2215-2223)
IEEE DOI 1711
Cameras, Laser noise, Machine learning, Measurement by laser beam, Sensors, Training 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
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

Weerasekera, C.S.[Chamara Saroj], Garg, R.[Ravi], Latif, Y.[Yasir], Reid, I.D.[Ian D.],
Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction,
ACCV18(V:609-624).
Springer DOI 1906
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.J.[Bo-Jin], Hua, Y.[Yan], Cai, A.N.[An-Ni],
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:Sep 1, 2022 at 11:00:56