Automatic photo pop-up

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Multiple View Geometry in Computer Vision

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Pattern Classification

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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

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Efficient Graph-Based Image Segmentation

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Light field rendering

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The lumigraph

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A grammar-based GUI for single view reconstruction

Khurana, D., Sankhla, S., Shukla, A., Varshney, R., Kalra, P. K., & Banerjee, S. (2012) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/2425333.2425347 · Cited by 1

Event Bank based multimedia representation via latent group logistic regression minimization

Liu, C., Li, D., Lu, B., & Xiong, J. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.12.002 · Cited by 1

Automatic Class-Specific 3D Reconstruction from a Single Image

Lozano‐Pérez, T., Kaelbling, L. P., & Chiu, H.-P. (2009) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 1

GEOMETRY AND TEXTURE MEASURES FOR INTERACTIVE VIRTUALIZED REALITY INDOOR MODELER

Thangamania, K., Ichikari, R., Okuma, T., Ishikawa, T., & Kurata, T. (2015) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xl-4-w5-43-2015 · Cited by 1

Global-Attributes Assisted Outdoor Scene Geometric Labeling

Chen, C., Ren, Y., & Kuo, C. ‐. J. (2016) · Springer briefs in electrical and computer engineering · doi:10.1007/978-981-10-0631-9_5 · Cited by 1

WebReview: An Intelligent Classification Platform to Automate the Evaluation and Ranking of Website Quality and Usability using Artificial Intelligence and Web Scraping Techniques

Xu, D., Xu, D., & Li, A. (2022) · doi:10.5121/csit.2022.120504 · Cited by 1

Novel Stereoscopic Content Production Tools

Hornung, A., Smolić, A., & Groß, M. H. (2010) · SMPTE International Conference on Stereoscopic 3D for Media and Entertainment · doi:10.5594/m001411 · Cited by 1

Estimation of absolute distance and height of people based on monocular view and deep neural networks for edge devices operating in the visible and thermal spectra

Gąsienica-Józkowy, J., Cyganek, B., Knapik, M., Głogowski, S., & Przebinda, Ł. (2023) · Annals of Computer Science and Information Systems · doi:10.15439/2023f3560 · Cited by 1

Parallax Photography: Creating 3D Motions from Stills

Zheng, K. C. (2009) · Cited by 1

Acquisition, Representation, Processing and Display of Digital Heritage Sites

Kalra, P. K., Kumar, S., & Banerjee, S. (2017) · Springer eBooks · doi:10.1007/978-981-10-5738-0_6 · Cited by 1

Stereo Generation from a Single Image Using Deep Residual Network

Huang, J., Bi, T., Liu, Y., & Wang, Y. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451311 · Cited by 1

Unsupervised Classemes

Cusano, C., Satta, R., & Santini, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33885-4_41 · Cited by 1

Deep Modular Network Architecture for Depth Estimation from Single Indoor Images

Ito, S., Kaneko, N., Shinohara, Y., & Sumi, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_19 · Cited by 1

SÜPERPİKSEL TABANLI SATIR BÖLÜTLEME

Demir, A. A., & Özkaya, U. (2019) · Mühendislik Bilimleri ve Tasarım Dergisi · doi:10.21923/jesd.520406 · Cited by 1

Latent 3D Volume for Joint Depth Estimation and Semantic Segmentation from a Single Image

Ito, S., Kaneko, N., & Sumi, K. (2020) · Sensors · doi:10.3390/s20205765 · Cited by 1

Depth Completion with Morphological Operations: An Intermediate Approach to Enhance Monocular Depth Estimation

Mendes, R. D. Q., Ribeiro, E. G., Rosa, N. D. S., & Grassi, V. (2020) · Latin American Robotics Symposium, Brazilian Symposium on Robotics and Workshop on Robotics in Education (LARS/SBR/WRE) · doi:10.1109/lars/sbr/wre51543.2020.9307124 · Cited by 1

Smaller Residual Network for Single Image Depth Estimation

HENDRA, A., & Kanazawa, Y. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7076 · Cited by 1

Adaptive 3-D scene construction from single image using extended object placement relation

Subpa-asa, A., Futragoon, N., & Kanongchaiyos, P. (2009) · ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry (VRCAI) · doi:10.1145/1670252.1670299 · Cited by 1

Object-based pseudo-three-dimensional conversion of two-dimensional videos

Jiang, J., & Xiao, G. (2009) · Optical Engineering · doi:10.1117/1.3159873 · Cited by 1

Model-Driven Video-Based Rendering for Vehicles

Sebe, I. O., You, S. O., & Neumann, U. (2006) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2006.126 · Cited by 1

Efficient Inference and Learning for Computer Vision Labelling Problems

Alahari, K. (2010) · Cited by 1

Silhouettes for calibration and reconstruction from multiple views

Pollefeys, M., & Sinha, S. N. (2009) · Cited by 1

Lifting GIS Maps into Strong Geometric Context.Preprint

Díaz, R. A., Lee, M., Schubert, J. E., & Fowlkes, C. C. (2015) · arXiv (Cornell University) · Cited by 1

Models for multi-view object class detection

Lozano‐Pérez, T., Kaelbling, L. P., & Chiu, H.-P. (2009) · Cited by 1

Authoring effective depictions of reality by combining multiple samples of the plenoptic function

Salesin, D., & Agarwala, A. (2006) · Cited by 1

SymmSLIC: Symmetry Aware Superpixel Segmentation and its ApplicationsPreprint

Nagar, R., & Raman, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09232 · Cited by 1

3D indoor topological modelling based on homotopy continuation

Jamali, A. (2017) · Cited by 1

Coupled Depth LearningPreprint

Baig, M. H., & Torresani, L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.04537 · Cited by 4

What Do Single-view 3D Reconstruction Networks Learn?Preprint

Tatarchenko, M., Richter, S. R., Ranftl, R., Li, Z., Koltun, V., & Brox, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.03678 · Cited by 4

Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched DataPreprint

Xu, J., Bai, Y., Liu, X., Jiang, J., & Ji, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.11573 · Cited by 1

Perception-Based Design for Tele-presence

Chaudhury, S., Ghosh, S., Basu, A., Lall, B., Roy, S. D., Choudhury, L., Prashanth, R., Singh, A., & Maniyar, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21786-9_27

Réalité Augmentée en extérieur: Enjeux et Etat de l'ArtPreprint

Zendjebil, I., Ababsa, F., Didier, J.-Y., Vairon, J., Frauciel, L., Hachet, M., Guitton, P., & Delmont, R. (2007)

Three Dimensional Reconstruction Using Vertical Constraints from a Photograph

Morita, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-17277-9_57

View Transformation Based on a Single Outdoor Image

Zhang, S., & Yan, S. Q. (2013) · International Journal of Advanced Robotic Systems · doi:10.5772/56357

Superpixel Segmentation of Outdoor Webcams to Infer Scene Structure

Tannenbaum, R. (2009) · Open Scholarship Institutional Repository (Washington University in St. Louis) · doi:10.7936/k7r20zdq

3D World from 2D Photos

Aoki, T., Tanikawa, T., & Hirose, M. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73345-4_29

Visual scene interpretation as a dialogue between vision and language

Yu, X., Fermuller, C. M., & Aloimonos, Y. (2011) · National Conference on Artificial Intelligence

VR Based Movie Watching Method by Reproduction of Spatial Sensation

Nishimura, K., Ito, A., Tanikawa, T., & Hirose, M. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02771-0_9

An Improved HDR Technique by Classification Method

Agrawal, D. J., & Shrivastava, S. (2015) · International Conference on Communication Systems and Network Technologies · doi:10.1109/csnt.2015.220

Fundamental Strategies for Solving Low-Level Vision Problems

Tappen, M. F. (2011) · IPSJ Transactions on Computer Vision and Applications · doi:10.2197/ipsjtcva.3.95

Scene projection by non-linear transforms to a geo-referenced map for situational awareness

Krucki, K., & Asari, V. K. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2077737

Photo magician: Controlling point of focus and depth of field on an all-focused image

Rahmah, D. N., Hua, K., Lai, Y., & Yao, C. (2013) · IEEE International Symposium on Consumer Electronics (ISCE) · doi:10.1109/isce.2013.6570253

A learning-based automatic spinal MRI segmentation

Liu, X., Samarabandu, J. K., Garvin, G. J., Chhem, R. K., & Li, S. (2008) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.769891

Contours-Matching in Building Reconstruction from a Single Image

Zheng, X., Zhang, X., Duan, F., & Liu, W. (2011) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis.2011.245

Stereo Pair Generation by Introducing Bokeh Effect in 2D Images

Chahal, N., & Chaudhury, S. (2015) · International Conference on Perception and Machine Intelligence (PerMIn) · doi:10.1145/2708463.2709069

Principles and Practice of Computer-Assisted Draft Object Modelling

Shi, Y., Hu, W., Xu, M. Z., Wu, P., & Zhou, Q. (2010) · International Conference on Challenges in Environmental Science and Computer Engineering · doi:10.1109/cesce.2010.243

A novel variety-based 3DTV content generation scheme for casually captured sparse photo collections

Sharma, M., Chaudhury, S., & Lall, B. (2013) · 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video · doi:10.1109/3dtv.2013.6676649

A probabilistic framework for geometry and motion reconstruction using prior information

Zhang, W., & Chen, T. (2010) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331209103038

Real-time scene structure reconstruction

Chen, I.-A., Wu, S.-C., Chang, K.-L., & Lin, C. H. (2014) · International Conference on Intelligent Green Building and Smart Grid · doi:10.1109/igbsg.2014.6835157

On Exploiting Affine Repetitions for 3D Reconstruction from a Single Image

Sharma, G., Chaudhury, S., & Srivastava, J. B. (2008) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1109/icvgip.2008.12

Superpixel Based Inpainting for Interactive 3D Indoor Modeler

Thangamani, K., Kurata, T., & Ishikawa, T. (2011) · Machine Vision and Applications

Single-View 3D Reconstruction by Learning 3D Game Scenes

Okabe, M., Anjyo, K., & Onai, R. (2014) · Mathematics for industry · doi:10.1007/978-4-431-55007-5_19

Learning to segment images into material and object classes

Ponce, J., & McHenry, K. G. (2008)

Combining recognition and geometry for data-driven 3D reconstruction

Owens, A. (2013) · DSpace@MIT (Massachusetts Institute of Technology)

From Images to Depths and Back

Hassner, T., & Basri, R. (2015) · Springer eBooks · doi:10.1007/978-3-319-23048-1_8

Inverse Rendering Techniques for Physically Grounded Image EditingPreprint

Karsch, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.00986

Virtual Passepartouts

Ritschel, T., Templin, K., Myszkowski, K., & Seidel, H. (2012) · doi:10.2312/pe/npar/npar12/057-063

DeepSymmetry: Joint Symmetry and Depth Estimation using Deep Neural Networks

Liu, G., Yang, C., Li, Z., Ceylan, D., & Qi-xing Huang, A. (2016)

Image Parallax Based Modeling of Depth-Layer Architecture

Hu, Y., Chu, B., & Qi, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16631-5_43

An Improved HDR Technique by Classification Method

Agarwal, D., Shrivastava, S., & Bisen, A. S. (2015) · International Journal of Computer Applications · doi:10.5120/21174-4087

Monocular-Cues Based 3-D Reconstruction

Tumu, S., Avasarala, V., Jonnalagadda, S. T., & Wadekar, P. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-0113-0.ch008

3D Imaging for Mapping and Inspection Applications in Outdoor Environments

Sukumar, S. R., Koschan, A. F., & Abidi, M. A. (2011) · IGI Global eBooks · doi:10.4018/978-1-61350-326-3.ch025

Image Based 3D Modeling and Rendering from Single View Perspective Images

Mohan, S., & Murali, S. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-3994-2.ch031

Co-Ordinate: Reconstruction of Three Dimensional Geometric Diagrams from a Single Image

Matsuyama, K., & Konno, K. (2014) · The Journal of the Society for Art and Science · doi:10.3756/artsci.13.85

TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS

Zheng, E. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/2k5x-9r30

State of the art of content-based image classification

Doukim, C. A., Dargham, J. A., & Chekima, A. (2014) · doi:10.1109/iccst.2014.7045199

On-line, Incremental Visual Scene Understanding for an Indoor Navigating Robot.

Tsai, G. (2014) · Deep Blue (University of Michigan)

Development of Semi-Automatic Methodology for Extraction of Depth for 2D-to-3D Conversion

Mulajkar, R. M., & Gohokar, V. V. (2017) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3055635.3056629

Development of methodology for depth estimation for images and videos

Mulajkar, R. M., & Gohokar, V. V. (2017) · International Journal of Engineering and Technology · doi:10.21817/ijet/2017/v9i3/1709030161

Monocular Fisheye Camera Depth Estimation Using Semi-supervised Sparse Velodyne Data.

Kumar, V. R., Milz, S., Simón, M., Witt, C., Amende, K., Petzold, J., & Yogamani, S. K. (2018)

Content-Based Image Recovery

Zhou, H.-Y., & Wu, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77383-4_59

EMF- MHCRF: Enhanced Median Filter (EMF) Based Noise Removal and Multilayer Hidden Conditional Random Field (MHCRF) Model for Dense Depth Map Reconstruction

Devi, P. (2017) · INTERNATIONAL JOURNAL OF EMERGING TRENDS IN SCIENCE AND TECHNOLOGY · doi:10.18535/ijetst/v4i8.35

Coupled Regression and Modeling of Depth from Single ViewPreprint

Baig, M. H., & Torresani, L. (2015) · arXiv (Cornell University)

Factoring Scene Layout From Monocular Images in Presence of Occlusion

Mitra, N. J. (2018) · Handbook of numerical analysis · doi:10.1016/bs.hna.2018.09.003

Single Image Depth Level Estimation Using Dark Channel Prior

Cao, Y., & Lu, X. (2018) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/435/1/012036

Spatio-Temporal Road Scene Reconstruction using Superpixel MRF.Preprint

Li, Y., Liu, Y., Zhu, J., Ma, S., Niu, Z., & Guo, R. (2018) · arXiv (Cornell University)

Addressing Latency Issues in 2D to 3D Conversion: Deploying Available Synthetic Database

Manasa, N. L. (2018) · International Journal of Informatics and Communication Technology (IJ-ICT) · doi:10.11591/ijict.v7i3.pp141-145

Recovering Depth from a Single Natural Image Based on Edge Blur Estimation

Cao, F.-Y., Yang, X., Qian, Y., & Shi, P.-B. (2018)

Spatio-Temporal Road Scene Reconstruction using Superpixel Markov Random FieldPreprint

Li, Y., Liu, Y., Zhu, J., Ma, S., Niu, Z., & Guo, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.09790

Novel Single View Constraints for Manhattan 3D Line ReconstructionPreprint

Ranade, S., & Ramalingam, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.03737

Video Depth-From-Defocus

Kim, H., Richardt, C., & Theobalt, C. (2016) · The University of Bath Online Publications Store (The University of Bath) · doi:10.48550/arxiv.1610.03782

Fine-scale Surface Normal Estimation using a Single NIR ImagePreprint

Yoon, Y., Choe, G., Kim, N. I., Lee, J., & Kweon, I. S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.07475

Deep 3D Pan via adaptive "t-shaped" convolutions with global and local adaptive dilationsPreprint

Bello, J. L. G., & Kim, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.01089

Depth Estimation from a Single Image using Multi Stream and Scale Deep Learning

Hendra, A., & Kanazawa, Y. (2019) · International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA) · doi:10.1109/icaicta.2019.8904227

Depth Estimation Using Convolutional Neural Network with Transfer Learning

Soni, U., & Yadav, H. N. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-7571-6_30

Predicting Relative Depth between Objects from Semantic FeaturesPreprint

Cassar, S., Muscat, A., & Seychell, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.04626

MonoER - A Edge Refined Self-Supervised Monocular Depth Estimation Method

Xiang, T., Zhao, L., Zhang, H., & Wang, Z. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9326510

Depth Estimation for Monocular Image Based on Convolutional Neural Networks

Niu, B., Tang, M., & Chen, X. (2021) · International Journal of Circuits Systems and Signal Processing · doi:10.46300/9106.2021.15.59

Learning Compositional Shape Priors for Few-Shot 3D ReconstructionPreprint

Michalkiewicz, M., Tsogkas, S., Parisot, S., Baktashmotlagh, M., Eriksson, A. P., & Belilovsky, E. (2021) · arXiv (Cornell University) · doi:10.2139/ssrn.4768661

Scene Uncertainty and the Wellington Posterior of Deterministic Image ClassifiersPreprint

Tsuei, S., Golatkar, A., & Soatto, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.13870

3D Model Reconstruction from Two Stereo Pair Images

Patel, K. N. (2018) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit217438

View Synthesis with Multi-scale Cost Aggregation and Confidence Prior

Wu, Q., Wang, X., & Wang, Q. (2021) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta52665.2021.9647048

Segmentation of X-Ray Images of Rocks Using Supervoxels Over-Segmentation

Alqahtani, H., Alqahtani, N. J., Armstrong, R. T., & Mostaghimi, P. (2022) · International Petroleum Technology Conference · doi:10.2523/iptc-22131-ms

Tangible images

Shahzad, R. (2014) · doi:10.32657/10356/61841

2D to 3D image and video conversion using machine learning

Conejero, J. L. H. (2020) · doi:10.20868/upm.thesis.66586

Art-Directed Composition in Dynamic Real Scenes

Petikam, L. (2021) · doi:10.26686/wgtn.14265254.v1

Towards Adversariality in Depth Estimation Based on Local Equilibrium Disparity Consensus and Ego-motionPreprint

Mekhzoum, H., Bourja, O., Abdelali, H. A., & Derrouz, H. (2022) · Research Square · doi:10.21203/rs.3.rs-1827874/v1

Short Literature Review for Visual Scene Understanding

Achirei, Ș.-D. (2021) · Buletinul Institutului Politehnic din Iaşi. Secţia Electrotehnică. Energetică. Electronică · doi:10.2478/bipie-2021-0017

Multi-View Human Tracking and 3D Localization in Retail

Jadhav, A. (2022) · International Conference on Artificial Intelligence and Machine Learning (CAIML) · doi:10.5121/csit.2022.121214

Towards Adversariality in Depth estimation based on local equilibrium disparity consensus and egomotionPreprint

Mekhzoum, H., Bourja, O., Abdelali, H. A., & Derrouz, H. (2022) · Research Square · doi:10.21203/rs.3.rs-1827874/v2

Cut-and-Fold: Automatic 3D modeling from a single image

Wang, G., Chen, X., & Chen, S. (2014) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew.2014.6890555 · Cited by 3

Automatic generation of 3D models from photographs

Pollefeys, M., Koch, R., Vergauwen, M., & Gool, L. V. (1998) · Cited by 10

Photo-inspired model-driven 3D object modeling

Xu, K., Zheng, H., Zhang, H., Cohen‐Or, D., Liu, L., & Xiong, Y. (2011) · ACM Transactions on Graphics · doi:10.1145/2010324.1964975 · Cited by 71

Multi‐style paper pop‐up designs from 3D models

Ruiz, C. R., Jr., Le, S. N., Yu, J., & Low, K. (2014) · Computer Graphics Forum · doi:10.1111/cgf.12320 · Cited by 29

Multiple Model Photo Popup

Bidwell, J., Decell, J., & Mokszycki, S. (2012)