Automatic photo pop-up

Untitled work

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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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Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)

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Use of the hough transformation to detect lines and curves in pictures

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Efficient Graph-Based Image Segmentation

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Light field rendering

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Multiple View Geometry in Computer Vision (2nd ed)

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The lumigraph

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Deep Learning Approaches for 3D Model Generation from 2D Artworks to Aid Blind People with Tactile Exploration

Furferi, R. (2024) · Heritage · doi:10.3390/heritage8010012 · Cited by 4

Test-Time Prompt Tuning for Zero-Shot Depth Completion

Jeong, C., Bae, I., Park, J.-H., & Jeon, H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00881 · Cited by 4

Semantic based autoencoder-attention 3D reconstruction network

Hu, F., Ye, L., Zhong, W., Fang, L., Tie, Y., & Zhang, Q. (2019) · Graphical Models · doi:10.1016/j.gmod.2019.101050 · Cited by 4

Innovative Technologies for Virtual Museums: An Overview

Furferi, R. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-20302-2_32 · Cited by 4

Texture-Driven Adaptive Mesh Refinement with Application to 3D Relief

Li, J., Wang, S., & Paquette, E. (2023) · Computer-Aided Design · doi:10.1016/j.cad.2023.103640 · Cited by 4

Learning to produce 3D media from a captured 2D video

Park, M., Luo, J., Gallagher, A., & Rabbani, M. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2072064 · Cited by 4

Peek‐in‐the‐Pic: Flying Through Architectural Scenes From a Single Image*

Shesh, A., & Chen, B. (2008) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2008.01199.x · Cited by 4

A method of feature selection using contribution ratio based on boosting

Tsuchiya, M., & Fujiyoshi, H. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761348 · Cited by 4

3D Reconstruction of a Collapsed Historical Site from Sparse Set of Photographs and Photogrammetric Map

Futragoon, N., Kitamoto, A., Andaroodi, E., Matini, M. R., & Ono, K. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22819-3_29 · Cited by 4

Unsupervised and Transfer Learning under Uncertainty - From Object Detections to Scene Categorization

Mesnil, G., Rifai, S., Bordes, A., Glorot, X., Bengio, Y., & Vincent, P. (2013) · International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0004227803450354 · Cited by 6

Normal Guided Data-Driven Semantic Modeling from a Single Indoor Image

Liu, M., Guo, Y., & Wang, J. (2016) · International Conference on Cyberworlds (CW) · doi:10.1109/cw.2016.23 · Cited by 4

An overview of recent progress in volumetric semantic 3D reconstruction

Häne, C., & Pollefeys, M. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900143 · Cited by 4

Visual Perception of Real World Depth Map Resolution for Mixed Reality Rendering

Petikam, L., Chalmers, A., & Rhee, T. J. (2018) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr.2018.8446391 · Cited by 4

Blocks-World Cameras

Lee, J., & Gupta, M. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01125 · Cited by 4

A System for Detecting Automated Parking Slots Using Deep Learning

Maharshi, R. T., Nagajyothi, D., Thrishul, P., & Reethika, P. (2023) · International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) · doi:10.1109/icdcece57866.2023.10150637 · Cited by 4

Binary-Patterns Based Floor Recognition Suitable for Urban Scenes

Osuna-Coutiño, J. A. D. J., & Martínez-Carranza, J. (2019) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit.2019.8820296 · Cited by 4

Single-image depth estimation by refined segmentation and consistency reconstruction

Liu, H., Lei, D., Zhu, Q., Sui, H., Zhang, H., & Wang, Z. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.116048 · Cited by 4

Track Compensation Algorithm Using Free Space Information with Occupancy Grid Map

Pae, D.-S., Jang, Y. S., Park, S. K., & Lim, M. T. (2021) · International Journal of Control Automation and Systems · doi:10.1007/s12555-020-0085-6 · Cited by 4

Automatic depth profiling of 2D cinema - and photographic images

Burazerovic, D., Vandewalle, P., & Berretty, R.-P. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5413347 · Cited by 4

Smart video sensors for 3D scene reconstruction of large infrastructures

Ripollés, O., Simó, J. E., Benet, G., & Vivó, R. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1184-z · Cited by 4

Example-Based Depth Generation from Single Image for 3D Content

Liu, K., Wu, Q., Chen, W., Wu, C.-F., Jan, F.-C., & Chen, T. (2008) · 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video · doi:10.1109/3dtv.2008.4547876 · Cited by 4

Superpixel Generation by Agglomerative Clustering With Quadratic Error Minimization

Dong, X., Chen, Z., Yao, J., & Guo, X. (2018) · Computer Graphics Forum · doi:10.1111/cgf.13538 · Cited by 4

Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidancePreprint

van Hecke, K. G., de Croon, G., van der Maaten, L., Hennes, D., & Izzo, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.08047 · Cited by 4

Layer-structured 3D Scene Inference via View SynthesisPreprint

Tulsiani, S., Tucker, R. P., & Snavely, N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.10264 · Cited by 4

Deep 3D-Zoom Net: Unsupervised Learning of Photo-Realistic 3D-ZoomPreprint

Bello, J. L. G., & Kim, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.09349 · Cited by 4

Automated methods for processing camera trap video data for distance sampling

Bak, T. M., Camp, R. J., Burt, M. D., & Vogt, S. (2025) · Pacific Conservation Biology · doi:10.1071/pc25008 · Cited by 3

Improvement of texture based image segmentation algorithm for HE stained tissue samples

Windisch, G., & Kozlovszky, M. (2013) · IEEE International Symposium on Computational Intelligence and Informatics (CINTI) · doi:10.1109/cinti.2013.6705205 · Cited by 3

Sewing Photos: Smooth Transition between Photos

Kuo, T.-H., Tsai, C.-Y., Cheng, K.-Y., & Chen, B. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-17832-0_8 · Cited by 3

Object Repositioning Based on the Perspective in a Single Image

Iizuka, S., Endo, Y., Hirose, M., Kanamori, Y., Mitani, J., & Fukui, Y. (2014) · Computer Graphics Forum · doi:10.1111/cgf.12423 · Cited by 3

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

What makes 2D-to-3D stereo conversion perceptually plausible?

Kellnhofer, P., Leimkühler, T., Ritschel, T., Myszkowski, K., & Seidel, H. (2015) · ACM Symposium on Applied Perception · doi:10.1145/2804408.2804409 · Cited by 3

Single Image Segmentation with Estimated Depth

Yonetani, R., Kimura, A., Sakano, H., & Fukuchi, K. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.28 · Cited by 3

Video 2‐D—to—3‐D conversion based on hybrid depth cueing

Cheng, C., Li, C., & Chen, L. (2010) · Journal of the Society for Information Display · doi:10.1889/jsid18.9.704 · Cited by 3

Fusion of dense spatial features and sparse temporal features for three‐dimensional structure estimation in urban scenes

Nawaf, M. M., & Trémeau, A. (2013) · IET Computer Vision · doi:10.1049/iet-cvi.2012.0270 · Cited by 3

Color and flow based superpixels for 3D geometry respecting meshing

Nawaf, M. M., Hasnat, A., Désiré Sidibé, A., & Trémeau, A. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836107 · Cited by 3

Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation

Atapour–Abarghouei, A., & Breckon, T. P. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-28603-3_2 · Cited by 3

Fast and Texture-Structure Preservable Inpainting and Its Application for Creating Virtualized-Reality Indoor Models

Thangamani, K., Ishikawa, T., Makita, K., & Kurata, T. (2012) · International Journal of Computer Science and Informatics · doi:10.47893/ijcsi.2012.1057 · Cited by 3

Convex Decomposition of Indoor Scenes

Vavilala, V., & Forsyth, D. A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00842 · Cited by 3

CFDepthNet: Monocular Depth Estimation Introducing Coordinate Attention and Texture Features

Feng, W., Zhu, J., Wang, H., & Shen, J. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11477-4 · Cited by 3

Surface Prediction for a Single Image of Urban Scenes

Akhmadeev, F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16628-5_27 · Cited by 3

Development of methodology for extraction of depth for 2D-to-3D conversion

Mulajkar, R. M., & Gohokar, V. V. (2017) · International Conference on Electrical, Computer and Communication Technologies (ICECCT) · doi:10.1109/icecct.2017.8117848 · Cited by 3

Depth Estimation From Single Image Through Multi-Path-Multi-Rate Diverse Feature Extractor

Lo, W.-Y., Chiu, C., & Luo, J.-Y. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054657 · Cited by 3

Panorama interpolation for image-based navigation

Shi, F. (2008) · doi:10.20381/ruor-12163 · Cited by 3

Homogeneous Superpixels from Markov Random Walks

Perbet, F., Stenger, B., & Maki, A. (2012) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e95.d.1740 · Cited by 3

3D structure inference by integrating segmentation and reconstruction from a single image

Lin, L., Zeng, K., Wang, Y., & Hu, W. (2008) · IET Computer Vision · doi:10.1049/iet-cvi:20065002 · Cited by 3

Towards optimal object bank for scene classification

Zhang, L., Xie, S., & Zhen, X. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6637997 · Cited by 3

A Probabilistic Framework for Geometry Reconstruction using Prior Information

Zhang, W., & Chen, T. (2007) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2007.4379209 · Cited by 3

Tour into the picture with water surface reflection and object movements

Park, J., Heo, N., Choi, S., & Shin, S. Y. (2006) · Computer Animation and Virtual Worlds · doi:10.1002/cav.135 · Cited by 3

Efficient view manipulation for cuboid-structured images

Guo, Y., Zhang, G., Lan, Z., & Wang, W. (2013) · Computers & Graphics · doi:10.1016/j.cag.2013.10.038 · Cited by 3

Enforcing Scene Constraints in Single View Reconstruction

Lourakis, M., & Argyros, A. (2007) · Eurographics · doi:10.2312/egs.20071030 · Cited by 3

Geo-registration of wide-baseline panoramic image sequences using a digital map reference

Kim, H., & Han, S. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3298-1 · Cited by 3

Outdoor Scene Classification Using Labeled Segments

Chen, C., Ren, Y., & Kuo, C. ‐. J. (2016) · Springer briefs in electrical and computer engineering · doi:10.1007/978-981-10-0631-9_4 · Cited by 3

Generating 3D models from a single 2D digitized photo using GIS and GroIMP

Chi, F., Kurth, W., & Smoleňová, K. (2016) · International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA) · doi:10.1109/fspma.2016.7818284 · Cited by 3

A Survey on Image-Based Approaches of Synthesizing Objects

Ren, H., Qiu, H., He, F., & Leng, K. (2016) · International Conference on Virtual Reality and Visualization · doi:10.1109/icvrv.2016.50 · Cited by 3

Estimating relative depth in single images via rankboost

Ewerth, R., Springstein, M., Müller-Budack, E., Balz, A., Gehlhaar, J., Naziyok, T., Dembczyński, K., & Hüllermeier, E. (2017) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2017.8019434 · Cited by 3

Content-Sensitive Superpixel Generation with Boundary Adjustment

Zhang, D., Xie, G., Ren, J., Zhang, Z., Bao, W., & Xu, X. (2020) · Applied Sciences · doi:10.3390/app10093150 · Cited by 3

Automatic building texture completion

Сергеевич, К. В., & Vezhnevets, V. (2007) · Cited by 3

A Binary Descriptor Invariant to Rotation and Robust to Noise (BIRRN) for Floor Recognition

Osuna-Coutiño, J. A. D. J., & Martínez-Carranza, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21077-9_25 · Cited by 3

Symmetry-aware Depth Estimation using Deep Neural NetworksPreprint

Liu, G., Yang, C., Li, Z., Ceylan, D., & Qi-xing Huang, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.06079 · Cited by 3

GraphX-Convolution for Point Cloud Deformation in 2D-to-3D ConversionPreprint

Nguyen, D. C., Choi, S., Kim, W., & Lee, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.06600 · Cited by 3

Edge-Guided Occlusion Fading Reduction for a Light-Weighted Self-Supervised Monocular Depth EstimationPreprint

Peng, K., Ditzler, G., & Rozenblit, J. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.11705 · Cited by 3

Discriminative high-level representations for scene classification

Zhang, L., Xie, S., & Zhen, X. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738895 · Cited by 2

PEEP: Perceptually Enhanced Exploration of Pictures

Agus, M., Villanueva, A. J., Pintore, G., & Gobbetti, E. (2016) · Eurographics · doi:10.2312/vmv.20161347 · Cited by 2

Camera Pose Estimation Using Collaborative Databases and Single Building Image

Semaan, B., Servières, M., Moreau, G., & Chebaro, B. (2020) · Journal of Geographic Information System · doi:10.4236/jgis.2020.126036 · Cited by 2

Object-Aware Monocular Depth Prediction With Instance Convolutions

Simsar, E., Örnek, E. P., Manhardt, F., Dhamo, H., Navab, N., & Tombari, F. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3155823 · Cited by 2

Image-Based OA-Style Paper Pop-Up Design via Mixed-Integer Programming

Huang, F., Liu, C., Hsiao, K., Kuo, Y.-M., Chu, H., & Yang, Y. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3189569 · Cited by 2

ARTE – Augmented Readability Tactile Exploration: The Tactile Bas-Relief of Piazza San Francesco Painting

Puggelli, L., Furferi, R., Governi, L., Santarelli, C., & Volpe, Y. (2022) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-17594-7_9 · Cited by 2

Robust depth completion based on Semantic Aggregation

Fu, Z., Li, X. S., Huai, T., Li, W., Dong, D., & He, L. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05366-5 · Cited by 2

Rapid geometric modeling for visual simulation using semi-automated reconstruction from single image

Feng, C., Deng, F., & Kamat, V. R. (2012) · Engineering With Computers · doi:10.1007/s00366-012-0283-9 · Cited by 2

Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian

Lee, J., Sung, M., Kim, H., & Kim, T. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01798 · Cited by 2

Joint Spatio-temporal Depth Features Fusion Framework for 3D Structure Estimation in Urban Environment

Nawaf, M. M., & Trémeau, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33885-4_53 · Cited by 2

Reconstructing Symmetric Curved Surfaces from a Single Image and Its Application

Zeng, J., Zhang, Y., Zhan, S., & Liu, C. (2006) · Lecture notes in computer science · doi:10.1007/11890881_23 · Cited by 2

Comprehensive depth estimation algorithm for efficient stereoscopic content creation in three-dimensional video systems

Xu, H., & Jiang, M. (2015) · Optical Engineering · doi:10.1117/1.oe.54.7.073103 · Cited by 2

Retrieving Images of Similar Geometrical Configuration

Zhang, X., & Li, B. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-17274-8_44 · Cited by 2

A System for Geometrically Constrained Single View Reconstruction

Lourakis, M. (2008) · Springer eBooks · doi:10.1007/978-3-540-79547-6_19 · Cited by 2

Rapid modeling of cones and cylinders from a single calibrated image using minimum 2D control points

Zhou, J., & Li, B. (2010) · Machine Vision and Applications · doi:10.1007/s00138-009-0241-8 · Cited by 2

Novel Stereoscopic Content Production Tools

Hornung, A., Smolić, A., & Groß, M. H. (2011) · SMPTE Motion Imaging Journal · doi:10.5594/j18041xy · Cited by 2

LabelMe: Online Image Annotation and Applications By developing a publicly available tool that allows users to use the Internet to quickly and easily annotate images, the authors were able to collect many detailed image descriptions.

Torralba, A. B., Russell, B. C., & Yuen, J. (2010) · Cited by 2

Pop-up Modelling of Hazy Scenes

Zhao, L., Hansard, M. E., & Cavallaro, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23231-7_28 · Cited by 2

A hierarchical inferential method for indoor scene classification

Jiang, J., Liu, P., Ye, Z., Zhao, W., & Tang, X. (2017) · International Journal of Applied Mathematics and Computer Science · doi:10.1515/amcs-2017-0059 · Cited by 2

A Scalable System to Synthesize Data for Natural Scene Text Localization and Recognition

Wu, Q., Chen, P., & Zhou, Y. (2019) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar47638.2019.9043965 · Cited by 2

Combining Monocular Geometric Cues with Traditional Stereo Cues for Consumer Camera Stereo

Kowdle, A., Gallagher, A., & Chen, T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33868-7_11 · Cited by 2

Strike a Pose: Image-based Pose Synthesis

Vanaken, C., Hermans, C., Mertens, T., Fiore, F. D., Bekaert, P., & Reeth, F. V. (2008) · Cited by 2

A 3D Pseudo-Reconstruction from Single ImageBased on Vanishing Point

Wang, J., Dong, F., Takegami, T., Go, E., & Hirota, K. (2009) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2009.p0393 · Cited by 2

Depth Estimation from Light Field Images via Convolutional Residual Network

Mun, J.-H., & Ho, Y. (2018) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipa.2018.8659639 · Cited by 2

Perception and Decision-Making of Autonomous Systems in the Era of Learning: An Overview.

Tang, Y. S., Zhao, C., Wang, J., Zhang, C., Sun, Q., & Qian, F. (2020) · Cited by 2

Rapid part-based 3D modeling

Sebe, I. O., You, S. O., & Neumann, U. (2005) · ACM Symposium on Virtual Reality Software and Technology (VRST) · doi:10.1145/1101616.1101646 · Cited by 2

Shape and Symmetry Induction for 3D ObjectsPreprint

Tulsiani, S., Kar, A., Qi-xing Huang, A., Carreira, J., & Malik, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.07845 · Cited by 2

Efficient and Scalable View Generation from a Single Image using Fully Convolutional NetworksPreprint

Bae, S., Elgharib, M. A., Hefeeda, M. M., & Matusik, W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.03737 · Cited by 2

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

Bello, J. L. G., & Kim, M. (2020) · arXiv (Cornell University) · Cited by 2

Automatic Scene Inference for 3D Object CompositingPreprint

Karsch, K., Sunkavalli, K., Hadap, S., Carr, N., Jin, H., Fonte, R., & Sittig, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12297 · Cited by 2

Salient Region Detection Based on Spectral Residual and Sparse Reconstruction Error

Liu, L. (2022) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac54830.2022.9929704 · Cited by 1

Non-linear integration of loss terms for improved new view synthesis

El‐Shazly, E. H., Abdelhakim, A. M., Zhang, X., & Fares, A. H. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16265-1 · Cited by 1

The Representation of Architectural Space for Caspar David Friedrich: The Case Study of Eldena Abbey

Esposito, A. (2025) · Arts · doi:10.3390/arts14010007 · Cited by 1

A methodology for feature based 3D face modelling from photographs

Abson, K., Ugail, H., & Ipson, S. (2008) · Bradford Scholars (University of Bradford) · Cited by 1

Motion segmentation and dense reconstruction of scenes containing moving objects observed by a moving camera

Medioni, G. G., & Chang, Y. (2007) · Cited by 1

Resolving Scale Ambiguity via XSlit Aspect Ratio Analysis

Yang, W. H., Lin, H., Kang, S. B., & Jingyi Yu, A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.391 · Cited by 1

Visualization of the Centre of Projection Geometrical Locus in a Single Image

Stojaković, V., Popov, S., & Tepavčević, B. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12253 · Cited by 1

Exploiting a scene calibration mechanism for depth estimation

Kazmi, S. M. A. M., Rao, N. I., Fazal, F., & Khan, M. F. (2012) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2012.6469572 · Cited by 1

A Single Texture Object 3-D Depth Recovery

Zhang, L., & Li, X. (2010) · International Workshop on Education Technology and Computer Science · doi:10.1109/etcs.2010.467 · Cited by 1

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)