The DOI matches its Crossref record; 31 of the 33 references it cites were found in a registry or an index.
Automatic photo pop-up
Abstract
This paper presents a fully automatic method for creating a 3D model from a single photograph. The model is made up of several texture-mapped planar billboards and has the complexity of a typical children's pop-up book illustration. Our main insight is that instead of attempting to recover precise geometry, we statistically model geometric classes defined by their orientations in the scene. Our algorithm labels regions of the input image into coarse categories: "ground", "sky", and "vertical". These labels are then used to "cut and fold" the image into a pop-up model using a set of simple assumptions. Because of the inherent ambiguity of the problem and the statistical nature of the approach, the algorithm is not expected to work on every image. However. it performs surprisingly well for a wide range of scenes taken from a typical person's photo album.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“As a baseline we compare against the pose network used by [62], which builds upon [76] with an additional scaling of the translation by the mean of the inverse depth.”
From Digging Into Self-Supervised Monocular Depth Estimation (2019) · cites this paper as [62]
Citations
Cites
(1992) · Cited by 23,431
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937655 · Cited by 8,216
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Use of the hough transformation to detect lines and curves in pictures
Duda, R. O., & Hart, P. E. (1972) · Communications of the ACM · doi:10.1145/361237.361242 · Cited by 6,633
Efficient Graph-Based Image Segmentation
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000022288.19776.77 · Cited by 6,205
Levoy, M., & Hanrahan, P. (1996) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/237170.237199 · Cited by 3,789
Multiple View Geometry in Computer Vision (2nd ed)
Hartley, R. I., & Zisserman, A. (2003) · Cambridge University Press eBooks · Cited by 2,747
Gortler, S. J., Grzeszczuk, R., Szeliski, R., & Cohen, M. F. (1996) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/237170.237200 · Cited by 2,459
Flagged first, then by citations
2 of the 33 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- 10.5555/839287.1515131 · DOI
Cited by
Sorted by citations
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Godard, C., Aodha, O. M., & Brostow, G. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.699 · Cited by 3,270
Digging Into Self-Supervised Monocular Depth Estimation
Godard, C., Aodha, O. M., Firman, M., & Brostow, G. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00393 · Cited by 2,561
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
Fan, H., Su, H., & Guibas, L. J. (2017) · 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/cvpr.2017.264 · Cited by 2,513
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer
Ranftl, R., Lasinger, K., Hafner, D., Konrad Schindler, B., & Koltun, V. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3019967 · Cited by 2,279
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
Choy, C., Xu, D., Gwak, J., Chen, K., & Savarese, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8_38 · Cited by 1,762
Synthetic Data for Text Localisation in Natural Images
Gupta, A., Vedaldi, A., & Zisserman, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.254 · Cited by 1,563
TurboPixels: Fast Superpixels Using Geometric Flows
Levinshtein, A., Stere, A., Kutulakos, K. N., Fleet, D. J., Dickinson, S., & Kaleem Siddiqi, A. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.96 · Cited by 1,146
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., & Koltun, V. (2022) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000462024 · Cited by 1,093
Robust Higher Order Potentials for Enforcing Label Consistency
Kohli, P., Ladický, Ľ., & Torr, P. H. S. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0202-0 · Cited by 858
What, where and who? Classifying events by scene and object recognition
Li, L.-J., & Fei-Fei, L. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408872 · Cited by 790
Kopf, J., Neubert, B., Chen, B. P., Cohen, M., Cohen‐Or, D., Deußen, O., Uyttendaele, M., & Lischinski, D. (2008) · ACM Transactions on Graphics · doi:10.1145/1409060.1409069 · Cited by 713
Recovering Surface Layout from an Image
Hoiem, D., Efros, A. A., & Hebert, M. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-0031-y · Cited by 678
Self-citationGeometric context from a single image
Hoiem, D., Efros, A. A., & Hebert, M. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.107 · Cited by 666
Self-citationView Synthesis by Appearance Flow
Zhou, T., Tulsiani, S., Sun, W., Malik, J., & Efros, A. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_18 · Cited by 630
Self-citation3-D Depth Reconstruction from a Single Still Image
Saxena, A., Chung, S. H., & Ng, A. Y. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0071-y · Cited by 600
Deep Stereo: Learning to Predict New Views from the World's Imagery
Flynn, J., Neulander, I., Philbin, J. F., & Snavely, N. (2016) · 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/cvpr.2016.595 · Cited by 599
Superpixels: An evaluation of the state-of-the-art
Stutz, D., Hermans, A., & Leibe, B. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.03.007 · Cited by 559
Category-Specific Video Summarization
Potapov, D., Douze, M., Harchaoui, Z., & Schmid, C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_35 · Cited by 521
Superpixel segmentation using Linear Spectral Clustering
Li, Z., & Chen, J. (2015) · 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/cvpr.2015.7298741 · Cited by 492
Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling
Karsch, K., Liu, C., & Kang, S. B. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2316835 · Cited by 453
Single image depth estimation from predicted semantic labels
Liu, B., Gould, S. J., & Koller, D. (2010) · 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/cvpr.2010.5539823 · Cited by 484
Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
Xu, D., Ricci, E., Ouyang, W., Wang, X., & Sebe, N. (2017) · 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/cvpr.2017.25 · Cited by 484
Content-preserving warps for 3D video stabilization
Liu, F., Gleicher, M. L., Jin, H., & Agarwala, A. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531350 · Cited by 468
Deep Depth Completion of a Single RGB-D Image
Zhang, Y., & Funkhouser, T. A. (2018) · 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/cvpr.2018.00026 · Cited by 445
LabelMe: Online Image Annotation and Applications
Torralba, A. B., Russell, B. C., & Yuen, J. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2050290 · Cited by 450
A Survey of Urban Reconstruction
Musialski, P., Wonka, P., Aliaga, D. G., Wimmer, M. H., Gool, L. V., & Purgathofer, W. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12077 · Cited by 435
What Do Single-View 3D Reconstruction Networks Learn?
Tatarchenko, M., Richter, S. R., Ranftl, R., Li, Z., Koltun, V., & Brox, T. (2019) · 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/cvpr.2019.00352 · Cited by 443
Single-Image Depth Perception in the WildPreprint
Chen, W., Zhao, F., Yang, D., & Deng, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.03901 · Cited by 418
A survey of graph theoretical approaches to image segmentation
Peng, B., Lei Zhang, A., & Zhang, D. D. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.09.015 · Cited by 391
Real-Time Superpixel Segmentation by DBSCAN Clustering Algorithm
Shen, J., Hao, X., Liang, Z., Liu, Y., Wang, W., & Shao, L. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2616302 · Cited by 385
Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation
Xu, D., Wang, W., Tang, H., Liu, H., Sebe, N., & Ricci, E. (2018) · 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/cvpr.2018.00412 · Cited by 358
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry
Yang, N., Wang, R., Stückler, J., & Cremers, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01237-3_50 · Cited by 346
Monocular depth estimation based on deep learning: An overview
Zhao, C., Sun, Q., Zhang, C., Tang, Y. S., & Qian, F. (2020) · Science China Technological Sciences · doi:10.1007/s11431-020-1582-8 · Cited by 344
Exploiting hierarchical context on a large database of object categories
Choi, M. J., Lim, J. J., Torralba, A. B., & Willsky, A. S. (2010) · 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/cvpr.2010.5540221 · Cited by 314
Transformation-Grounded Image Generation Network for Novel 3D View Synthesis
Park, E., Yang, J., Yumer, E., Ceylan, D., & Berg, A. C. (2017) · 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/cvpr.2017.82 · Cited by 282
Depth Extraction from Video Using Non-parametric Sampling
Karsch, K., Liu, C., & Kang, S. B. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33715-4_56 · Cited by 269
A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images
Li, J., Klein, R., & Yao, A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.365 · Cited by 259
A review of virtual environments for training in ball sports
Miles, H. C., Pop, S. R., Watt, S. J., Lawrence, G. P., & John, N. W. (2012) · Computers & Graphics · doi:10.1016/j.cag.2012.04.007 · Cited by 270
Piecewise planar stereo for image-based rendering
Sinha, S. N., Steedly, D., & Szeliski, R. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459417 · Cited by 243
PlaneRCNN: 3D Plane Detection and Reconstruction From a Single Image
Liu, C., Kim, K., Gu, J., Furukawa, Y., & Kautz, J. (2019) · 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/cvpr.2019.00458 · Cited by 240
PanoContext: A Whole-Room 3D Context Model for Panoramic Scene Understanding
Zhang, Y., Song, S., Tan, P., & Xiao, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_43 · Cited by 239
Marr Revisited: 2D-3D Alignment via Surface Normal Prediction
Bansal, A., Russell, B., & Gupta, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.642 · Cited by 213
Zhu, H., Meng, F., Cai, J., & Lu, S. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.10.012 · Cited by 238
Rendering synthetic objects into legacy photographs
Karsch, K., Hedau, V., Forsyth, D. A., & Hoiem, D. (2011) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/2070752.2024191 · Cited by 229
Self-citationLearning 3-D Scene Structure from a Single Still Image
Saxena, A., Sun, M., & Ng, A. Y. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408828 · Cited by 224
Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss
Jiao, J., Cao, Y. S., Song, Y., & Lau, R. W. H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01267-0_4 · Cited by 218
Linear Spectral Clustering Superpixel
Chen, J., Li, Z., & Huang, B. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2651389 · Cited by 207
Recovering Occlusion Boundaries from a Single Image
Hoiem, D., Stein, A. N., Efros, A. A., & Hebert, M. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408985 · Cited by 206
Self-citationRobust higher order potentials for enforcing label consistency
Kohli, P., Ladický, Ľ., & Torr, P. H. S. (2008) · 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/cvpr.2008.4587417 · Cited by 203
Cheng, M., Zhang, F., Mitra, N. J., Huang, X., & Hu, S. (2010) · ACM Transactions on Graphics · doi:10.1145/1778765.1778820 · Cited by 194
Evaluation of super-voxel methods for early video processing
Xu, C., & Corso, J. J. (2012) · 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/cvpr.2012.6247802 · Cited by 193
3D Ken Burns effect from a single image
Niklaus, S., Mai, L., Yang, J., & Liu, F. (2019) · ACM Transactions on Graphics · doi:10.1145/3355089.3356528 · Cited by 191
Rendering synthetic objects into legacy photographs
Karsch, K., Hedau, V., Forsyth, D. A., & Hoiem, D. (2011) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/2024156.2024191 · Cited by 192
Self-citationAutomatic Scene Inference for 3D Object Compositing
Karsch, K., Sunkavalli, K., Hadap, S., Carr, N., Jin, H., Fonte, R., Sittig, M., & Forsyth, D. A. (2014) · ACM Transactions on Graphics · doi:10.1145/2602146 · Cited by 180
Learning Depth From Single Images With Deep Neural Network Embedding Focal Length
He, L., Wang, G., & Hu, Z. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2832296 · Cited by 178
Recovering Occlusion Boundaries from an Image
Hoiem, D., Efros, A. A., & Hebert, M. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-010-0400-4 · Cited by 177
Self-citationSynthetic depth-of-field with a single-camera mobile phone
Wadhwa, N., Garg, R. D., Jacobs, D. E., Feldman, B. E., Kanazawa, N., Carroll, R., Movshovitz-Attias, Y., Barron, J. T., Pritch, Y., & Levoy, M. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201329 · Cited by 174
3D building modeling using images and LiDAR: a review
Wang, R. (2013) · International Journal of Image and Data Fusion · doi:10.1080/19479832.2013.811124 · Cited by 172
Image-based street-side city modeling
Xiao, J., Fang, T., Zhao, P., Lhuillier, M., & Quan, L. (2009) · ACM Transactions on Graphics · doi:10.1145/1618452.1618460 · Cited by 172
Single-Image Depth Estimation Based on Fourier Domain Analysis
Lee, J.-H., Heo, M., Kim, K.-R., & Kim, C. (2018) · 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/cvpr.2018.00042 · Cited by 171
Estimating the Natural Illumination Conditions from a Single Outdoor Image
Lalonde, J., Efros, A. A., & Narasimhan, S. G. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0501-8 · Cited by 161
Self-citationT$$^2$$Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks
Zheng, C., Cham, T., & Cai, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_47 · Cited by 154
Image dehazing using polarization effects of objects and airlight
Fang, S., Xia, X., Huo, X., & Chen, C. W. (2014) · Optics Express · doi:10.1364/oe.22.019523 · Cited by 148
Machines that learn to segment images: a crucial technology for connectomics
Jain, V., Seung, H. S., & Turaga, S. C. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.07.004 · Cited by 142
Joint Optimization for Object Class Segmentation and Dense Stereo Reconstruction
Ladický, Ľ., Sturgess, P., Russell, C., Sengupta, S., Baştanlar, Y., Clocksin, W. F., & Torr, P. H. S. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0489-0 · Cited by 142
Objects as Attributes for Scene Classification
Li, L.-J., Su, H., Lim, Y., & Fei-Fei, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35749-7_5 · Cited by 142
Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image
Huang, S., Qi, S., Zhu, Y., Xiao, Y., Xu, Y., & Zhu, S. C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_12 · Cited by 141
BA-Net: Dense Bundle Adjustment NetworkPreprint
Tang, C., & Tan, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04807 · Cited by 155
Closing the loop in scene interpretation
Hoiem, D., Efros, A. A., & Hebert, M. (2008) · 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/cvpr.2008.4587587 · Cited by 132
Self-citation3D video and free viewpoint video—From capture to display
Smolić, A. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.09.005 · Cited by 132
Kopf, J., Neubert, B., Chen, B. P., Cohen, M., Cohen‐Or, D., Deußen, O., Uyttendaele, M., & Lischinski, D. (2008) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/1457515.1409069 · Cited by 132
Example Based 3D Reconstruction from Single 2D Images
Hassner, T., & Basri, R. (2006) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2006.76 · Cited by 130
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth
Facil, J. M., Ummenhofer, B., Zhou, H., Montesano, L., Brox, T., & Civera, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01210 · Cited by 130
Synthetic Data for Text Localisation in Natural ImagesPreprint
Gupta, A., Vedaldi, A., & Zisserman, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.06646 · Cited by 165
BA-Net: Dense Bundle Adjustment Networks
Tang, C., & Tan, P. (2018) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 129
Content-preserving warps for 3D video stabilization
Liu, F., Gleicher, M. L., Jin, H., & Agarwala, A. (2009) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1576246.1531350 · Cited by 126
Three-Dimensional Video Postproduction and Processing
Smolić, A., Kauff, P. P., Knorr, S. B., Hornung, A., Kunter, M., Muller, M., & Lang, M. (2011) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2098350 · Cited by 126
Learning Single Camera Depth Estimation Using Dual-Pixels
Garg, R. D., Wadhwa, N., Ansari, S., & Barron, J. T. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00772 · Cited by 131
Efficient structured prediction for 3D indoor scene understanding
Schwing, A. G., Hazan, T., Pollefeys, M., & Urtasun, R. (2012) · 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/cvpr.2012.6248006 · Cited by 123
Single image depth estimation: An overview
Mertan, A., Duff, D. J., & Unal, G. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103441 · Cited by 121
Single View Reconstruction of Curved Surfaces
Prasad, M., & Fitzgibbon, A. W. (2006) · 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/cvpr.2006.281 · Cited by 115
Novel View Synthesis of Dynamic Scenes With Globally Coherent Depths From a Monocular Camera
Yoon, J. S., Kim, K., Gallo, O., Park, H. S., & Kautz, J. (2020) · 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/cvpr42600.2020.00538 · Cited by 119
Learning Object-to-Class Kernels for Scene Classification
Zhang, L., Zhen, X., & Shao, L. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2328894 · Cited by 110
Coherent intrinsic images from photo collections
Laffont, P.-Y., Bousseau, A., Paris, S., Durand, F., & George Drettakis, B. (2012) · ACM Transactions on Graphics · doi:10.1145/2366145.2366221 · Cited by 109
Monocular Depth Estimation Using Multi-Scale Continuous CRFs as Sequential Deep Networks
Xu, D., Ricci, E., Ouyang, W., Wang, X., & Sebe, N. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2839602 · Cited by 104
Zheng, Y., Chen, X., Cheng, M., Zhou, K., Hu, S., & Mitra, N. J. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185595 · Cited by 109
Symmetric architecture modeling with a single image
Jiang, N., Tan, P., & Cheong, L. (2009) · ACM Transactions on Graphics · doi:10.1145/1618452.1618459 · Cited by 107
Efficient Exact Inference for 3D Indoor Scene Understanding
Schwing, A. G., & Urtasun, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33783-3_22 · Cited by 104
Data-driven shape analysis and processing
Xu, K., Kim, V. G., Huang, Q., Mitra, N. J., & Kalogerakis, E. (2016) · SIGGRAPH Asia Courses · doi:10.1145/2988458.2988473 · Cited by 107
Geometric Image Parsing in Man-Made Environments
Tretyak, E., Barinova, O., Kohli, P., & Lempitsky, V. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0488-1 · Cited by 103
Single-view hair modeling for portrait manipulation
Chai, M., Wang, L., Weng, Y., Yu, Y., Guo, B., & Zhou, K. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185612 · Cited by 102
Superpixel Endmember Detection
Thompson, D. R., Mandrake, L., Gilmore, M. S., & Castaño, R. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2010.2070802 · Cited by 103
Chen, X., Kang, S. B., Xu, Y., Dorsey, J. O., & Shum, H. (2008) · ACM Transactions on Graphics · doi:10.1145/1356682.1356684 · Cited by 102
Dual Attention Guided Gaze Target Detection in the Wild
Fang, Y., Tang, J., Wang, S., Shen, W., Gu, X., Song, L., & Zhai, G. (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/cvpr46437.2021.01123 · Cited by 106
Mou, L., & Zhu, X. X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.10249 · Cited by 114
Multi-modal Auto-Encoders as Joint Estimators for Robotics Scene Understanding
Cadena, C., Dick, A., & D. Reid, I. (2016) · Robotics: Science and Systems XII · doi:10.15607/rss.2016.xii.041 · Cited by 93
Dynamic hair manipulation in images and videos
Chai, M., Wang, L., Weng, Y., Jin, X., & Zhou, K. (2013) · ACM Transactions on Graphics · doi:10.1145/2461912.2461990 · Cited by 92
360MonoDepth: High-Resolution 360° Monocular Depth Estimation
Rey-Area, M., Yuan, M., & Richardt, C. (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/cvpr52688.2022.00374 · Cited by 93
A novel 2Dd-to-3D conversion system using edge information
Cheng, C., Li, C., & Chen, L. (2010) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2010.5606320 · Cited by 87
Active refocusing of images and videos
Moreno-Noguer, F., Belhumeur, P. N., & Nayar, S. K. (2007) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1275808.1276461 · Cited by 86
Metadata
- Authors
- Hoiem, D., Efros, A. A., & Hebert, M.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- ACM Transactions on Graphics
- Published
- 2005-07
- Volume
- 24
- Issue
- 3
- Pages
- 577-584
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2151996626
- Cited by
- 616
- References
- 33
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 7 · 2023: 7 · 2022: 23 · 2021: 25 · 2020: 30 · 2019: 48 · 2018: 59 · 2017: 33 · 2016: 43 · 2015: 38 · 2014: 39 · 2013: 36 · 2012: 33
- FWCI
- 24.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Graphics (publishedVersion)
- KiltHub Repository (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- automatic 3D model generation, single-image 3D reconstruction, image segmentation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review