The DOI matches its Crossref record; 25 of the 28 references it cites were found in a registry or an index.
Dense Reconstruction Using 3D Object Shape Priors
Abstract
We propose a formulation of monocular SLAM which combines live dense reconstruction with shape priors-based 3D tracking and reconstruction. Current live dense SLAM approaches are limited to the reconstruction of visible surfaces. Moreover, most of them are based on the minimisation of a photo-consistency error, which usually makes them sensitive to specularities. In the 3D pose recovery literature, problems caused by imperfect and ambiguous image information have been dealt with by using prior shape knowledge. At the same time, the success of depth sensors has shown that combining joint image and depth information drastically increases the robustness of the classical monocular 3D tracking and 3D reconstruction approaches. In this work we link dense SLAM to 3D object pose and shape recovery. More specifically, we automatically augment our SLAM system with object specific identity, together with 6D pose and additional shape degrees of freedom for the object(s) of known class in the scene, combining image data and depth information for the pose and shape recovery. This leads to a system that allows for full scaled 3D reconstruction with the known object(s) segmented from the scene. The segmentation enhances the clarity, accuracy and completeness of the maps built by the dense SLAM system, while the dense 3D data aids the segmentation process, yielding faster and more reliable convergence than when using 2D image data alone.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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
“Prior information about a scene has increasingly been shown to provide a significant boost to SLAM systems.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [56]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Parallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346
KinectFusion: Real-time dense surface mapping and tracking
Newcombe, R. A., Fitzgibbon, A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., Kohi, P., Shotton, J., & Hodges, S. (2011) · 2011 10th IEEE International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2011.6092378 · Cited by 4,008
MonoSLAM: Real-time single camera SLAM
Davison, A. J., Reid, I. D., Molton, N. D., & Stasse, O. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1049 · Cited by 3,959
Self-citationA volumetric method for building complex models from range images
Curless, B., & Levoy, M. (1996) · Proceedings of the 23rd annual conference on Computer graphics and interactive techniques · doi:10.1145/237170.237269 · Cited by 3,124
DTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078
Fast cost-volume filtering for visual correspondence and beyond
Hosni, A., Rhemann, C., Bleyer, M., Rother, C., & Gelautz, M. (2011) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2011.5995372 · Cited by 1,193
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
Black, M. J., & Jepson, A. D. (1998) · International Journal of Computer Vision · doi:10.1023/a:1007939232436 · Cited by 1,038
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
Lawrence, N. D. (2005) · Cited by 906
A shape-based approach to the segmentation of medical imagery using level sets
Tsai, A., Yezzi, A. J., Wells, W. M., Tempany, C. M., Tucker, D., Fan, A., Grimson, W. E. L., & Willsky, A. S. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.808355 · Cited by 895
Research works printed but not linked: 2
- lieberknecht, 2011, RGBD camera-based parallel tracking and meshing, ISMAR, 147
- rodriguez-molina, 2011, LibPaBOD A Library for Part-based Object Detection in C++
Cited by
Sorted by citations
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596
Self-citationPast, present, and future of simultaneous localization and mapping: Toward the robust-perception age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I. D., & Leonard, J. J. (2016) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-138892 · Cited by 3,094
Self-citation3D-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
Computer Vision for Autonomous Vehicles
Janai, J., Güney, F., Behl, A., & Geiger, A. (2020) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000079 · Cited by 707
CubeSLAM: Monocular 3-D Object SLAM
Yang, S., & Scherer, S. (2019) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2909168 · Cited by 511
Gkioxari, G., Johnson, J., & Malik, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00988 · Cited by 486
JOINT 3D ESTIMATION OF VEHICLES AND SCENE FLOW
Menze, M., Heipke, C., & Geiger, A. (2015) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-ii-3-w5-427-2015 · Cited by 341
Learning a Multi-View Stereo MachinePreprint
Kar, A., Häne, C., & Malik, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.05375 · Cited by 427
Hierarchical Surface Prediction for 3D Object Reconstruction
Häne, C., Tulsiani, S., & Malik, J. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00054 · Cited by 308
Implicit Surface Representations As Layers in Neural Networks
Michalkiewicz, M., Pontes, J. K., Jack, D., Baktashmotlagh, M., & Eriksson, A. P. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00484 · Cited by 251
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art
Janai, J., Güney, F., Behl, A., & Geiger, A. (2020) · doi:10.1561/9781680836899 · Cited by 244
Learning 3D Shape Completion from Laser Scan Data with Weak Supervision
Stutz, D., & Geiger, 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.2018.00209 · Cited by 231
Displets: Resolving stereo ambiguities using object knowledge
Güney, F., & Geiger, A. (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.2015.7299044 · Cited by 217
Vicente, S., Carreira, J., Agapito, L., & Batista, J. P. (2014) · 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.2014.13 · Cited by 213
Menze, M., Heipke, C., & Geiger, A. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.09.013 · Cited by 192
Dense 3D Object Reconstruction from a Single Depth View
Yang, B., Rosa, S., Markham, A., Trigoni, N., & Wen, H. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2868195 · Cited by 148
Weakly Supervised 3D Reconstruction with Adversarial Constraint
Gwak, J., Choy, C., Chandraker, M. K., Garg, A., & Savarese, S. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00038 · Cited by 118
Semantics for Robotic Mapping, Perception and Interaction: A Survey
Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., Wu, Q., Chin, T.-J., Reid, I. D., Gould, S. J., et al. (2020) · Foundations and Trends in Robotics · doi:10.1561/2300000059 · Cited by 122
Self-citationNodeSLAM: Neural object descriptors for multi-view shape reconstruction
Sucar, E., Wada, K., & Davison, A. (2020) · 2020 International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00105 · Cited by 108
A Field Model for Repairing 3D Shapes
Nguyen, D. T., Hua, B., Tran, M.-K., Pham, Q.-H., & Yeung, S.-K. (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.2016.612 · Cited by 108
3D Object Localisation from Multi-view Image Detections
Rubino, C., Crocco, M., & Bue, A. D. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2701373 · Cited by 103
DSP-SLAM: Object Oriented SLAM with Deep Shape Priors
Wang, J., Rünz, M., & Agapito, L. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00143 · Cited by 94
Improved Adversarial Systems for 3D Object Generation and ReconstructionPreprint
Smith, E., & Meger, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.09557 · Cited by 94
Next-Generation Augmented Reality Browsers: Rich, Seamless, and Adaptive
Langlotz, T., Nguyen, T., Schmalstieg, D., & Grasset, R. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2013.2294255 · Cited by 91
Good features to track for visual SLAM
Zhang, G., & Vela, P. (2015) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2015.7298743 · Cited by 85
Learning 3D Shape Completion under Weak Supervision
Stutz, D., & Geiger, A. (2018) · MPG.PuRe (Max Planck Society) · Cited by 86
Qi, Y., Zhou, Y., Liu, Y., Liu, L., & Pan, Z. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3083065 · Cited by 84
Deep Level Sets: Implicit Surface Representations for 3D Shape InferencePreprint
Michalkiewicz, M., Pontes, J. K., Jack, D., Baktashmotlagh, M., & Eriksson, A. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.06802 · Cited by 107
Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age.Preprint
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I. D., & Leonard, J. J. (2016) · arXiv (Cornell University) · Cited by 78
Self-citationAnalysis and synthesis of 3D shape families via deep‐learned generative models of surfaces
Huang, H., Kalogerakis, E., & Marlin, B. M. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12694 · Cited by 74
Learning 3D Shape Completion Under Weak Supervision
Stutz, D., & Geiger, A. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1126-y · Cited by 71
Semantic alignment of LiDAR data at city scale
Yu, F., Xiao, J., & Funkhouser, T. A. (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.2015.7298781 · Cited by 71
Mesh R-CNNPreprint
Gkioxari, G., Malik, J., & Johnson, J. (2019) · arXiv (Cornell University) · Cited by 71
Localization from semantic observations via the matrix permanent
Atanasov, N., Zhu, M., Daniilidis, K., & Pappas, G. J. (2015) · The International Journal of Robotics Research · doi:10.1177/0278364915596589 · Cited by 69
2D-3D Pose Estimation of Heterogeneous Objects Using a Region Based Approach
Hexner, J., & Hagege, R. R. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0873-2 · Cited by 65
Analysis and synthesis of 3D shape families via deep-learned generative models of surfaces
Huang, H., Kalogerakis, E., & Marlin, B. M. (2015) · doi:10.5555/2853908.2853912 · Cited by 64
FroDO: From Detections to 3D Objects
Rünz, M., Li, K., Tang, M., Ma, L., Kong, C., Schmidt, T., Reid, I. D., Agapito, L., Straub, J., Lovegrove, S. J., et al. (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/cvpr42600.2020.01473 · Cited by 69
Self-citationRecovering Stable Scale in Monocular SLAM Using Object-Supplemented Bundle Adjustment
Frost, D., Prisacariu, V. A., & Murray, D. W. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2820722 · Cited by 61
Self-citationReal-Time Dense Monocular SLAM With Online Adapted Depth Prediction Network
Luo, H., Gao, Y., Wu, Y., Liao, C., Yang, X. Y., & Cheng, K.-T. T. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2859034 · Cited by 60
Joint Object Pose Estimation and Shape Reconstruction in Urban Street Scenes Using 3D Shape Priors
Engelmann, F., Stückler, J., & Leibe, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45886-1_18 · Cited by 60
Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction
Fragkiadaki, K., Salas, M., Arbeláez, P., & Malik, J. (2014) · Cited by 58
Object-aware bundle adjustment for correcting monocular scale drift
Frost, D., Kähler, O., & Murray, D. W. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487680 · Cited by 56
DymSLAM: 4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation
Wang, C., Luo, B., Zhang, Y., Zhao, Q. Z., Yin, L., Wang, W., Su, X. B., Wang, Y., & Li, C. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3045647 · Cited by 54
Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels
Ulusoy, A. O., Black, M. J., & Geiger, A. (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.2017.482 · Cited by 51
Zhang, Z., Zhao, R., Liu, E., Yan, K., & Ma, Y. (2018) · Sensors · doi:10.3390/s18061948 · Cited by 50
A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image
Zhao, R., Wang, Y., & Martı́nez, A. M. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2772922 · Cited by 47
Li, W., Viola, F., Starck, J., Brostow, G., & Campbell, N. D. F. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925973 · Cited by 45
SAMP: Shape and Motion Priors for 4D Vehicle Reconstruction
Engelmann, F., Stuckler, J., & Leibe, B. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.51 · Cited by 42
Reactive obstacle avoidance of monocular quadrotors with online adapted depth prediction network
Yang, X. Y., Luo, H., Wu, Y., Gao, Y., Liao, C., & Cheng, K.-T. T. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.10.019 · Cited by 40
C2FNet: A Coarse-to-Fine Network for Multi-View 3D Point Cloud Generation
Lei, J., Song, J., Peng, B., Li, W., Pan, Z., & Huang, Q. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3203213 · Cited by 40
Class Specific 3D Object Shape Priors Using Surface Normals
Häne, C., Savinov, N., & Pollefeys, M. (2014) · 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.2014.89 · Cited by 39
Probabilistic Structure from Motion with Objects (PSfMO)
Gay, P., Bansal, V., Rubino, C., & Bue, A. D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.334 · Cited by 39
Wang, R., Yang, N., Stückler, J., & Cremers, D. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197095 · Cited by 35
Scaling CNNs for High Resolution Volumetric Reconstruction from a Single Image
Johnston, A., Garg, R., Carneiro, G., Reid, I. D., & van den Hengel, A. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.114 · Cited by 35
Self-citationEfficient Dense Point Cloud Object Reconstruction Using Deformation Vector Fields
Li, K., Pham, T. T., Zhan, H., & Reid, I. D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_31 · Cited by 34
Self-citationScene Depth Perception Based on Omnidirectional Structured Light
Jia, T., Wang, B., Zhou, Z., & Meng, H. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2590304 · Cited by 32
Wide-Baseline Multi-Camera Calibration using Person Re-Identification
Xu, Y. L., Li, Y.-J., Weng, X., & Kitani, K. (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/cvpr46437.2021.01293 · Cited by 31
Weakly-Supervised Learning of Category-Specific 3D Object Shapes
Han, J., Yang, Y., Zhang, D., Huang, D., Xu, D., & la Torre, F. D. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2949562 · Cited by 30
Patches, Planes and Probabilities: A Non-Local Prior for Volumetric 3D Reconstruction
Ulusoy, A. O., Black, M. J., & Geiger, A. (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.2016.357 · Cited by 29
Manhattan and Piecewise-Planar Constraints for Dense Monocular Mapping
Concha, A., Hussain, W., Montano, L., & Civera, J. (2014) · Robotics: Science and Systems · doi:10.15607/rss.2014.x.016 · Cited by 28
Weakly Supervised Generative Adversarial Networks for 3D Reconstruction.Preprint
Gwak, J., Choy, C., Garg, A., Chandraker, M. K., & Savarese, S. (2017) · arXiv (Cornell University) · Cited by 27
Sparse depth sensing for resource-constrained robots
Ma, F., Carlone, L., Ayaz, U., & Karaman, S. (2019) · The International Journal of Robotics Research · doi:10.1177/0278364919850296 · Cited by 25
Virtual view networks for object reconstruction
Carreira, J., Kar, A., Tulsiani, S., & Malik, 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.2015.7298912 · Cited by 24
Unsupervised Dense Object Discovery, Detection, Tracking and Reconstruction
Ma, L., & Sibley, G. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10605-2_6 · Cited by 23
Dense monocular reconstruction using surface normals
Weerasekera, C. S., Latif, Y., Garg, R., & Reid, I. D. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989293 · Cited by 23
Self-citationContour-SLAM: A Robust Object-Level SLAM Based on Contour Alignment
Lin, S., Wang, J., Xu, M., Zhao, H., & Chen, Z. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3244223 · Cited by 21
Wang, S., Kobayashi, Y., Ravankar, A. A., Ravankar, A. A., & Emaru, T. (2019) · Sensors · doi:10.3390/s19102230 · Cited by 21
The role of prior in image based 3D modeling: a survey
Zhu, H., Nie, Y., Yue, T., & Cao, X. (2016) · Frontiers of Computer Science · doi:10.1007/s11704-016-5520-8 · Cited by 21
Incorporating scene priors to dense monocular mapping
Concha, A., Hussain, W., Montano, L., & Civera, J. (2015) · Autonomous Robots · doi:10.1007/s10514-015-9465-9 · Cited by 20
Simultaneous localization, mapping, and manipulation for unsupervised object discovery
Ma, L., Ghafarianzadeh, M., Coleman, D., Correll, N., & Sibley, G. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139365 · Cited by 20
2D or Not 2D: Bridging the Gap Between Tracking and Structure from Motion
Lebeda, K., Hadfield, S., & Bowden, R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16817-3_42 · Cited by 20
In-Vehicle Object-Level 3D Reconstruction of Traffic Scenes
Rao, Q., & Chakraborty, S. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3008080 · Cited by 20
MOLTR: Multiple Object Localization, Tracking and Reconstruction From Monocular RGB Videos
Li, K., Rezatofighi, H., & Reid, I. D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3061080 · Cited by 20
Self-citationObject Proposals Estimation in Depth Image Using Compact 3D Shape Manifolds
Zheng, S., Prisacariu, V. A., Averkiou, M., Cheng, M., Mitra, N. J., Shotton, J., Torr, P. H. S., & Rother, C. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24947-6_16 · Cited by 18
Self-citationLearning 3D Object Shape and Layout without 3D Supervision
Gkioxari, G., Ravi, N., & Johnson, J. (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/cvpr52688.2022.00174 · Cited by 19
Hierarchical Surface Prediction
Häne, C., Tulsiani, S., & Malik, J. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2896296 · Cited by 17
Dense mapping from an accurate tracking SLAM
Huang, W., Zhang, G., & Han, X. (2020) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003357 · Cited by 17
Monocular scene flow estimation via variational method
Xiao, D., Yang, Q., Yang, B., & Wei, W. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3091-6 · Cited by 16
Lifting Object Detection Datasets into 3D
Carreira, J., Vicente, S., Agapito, L., & Batista, J. P. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2435707 · Cited by 17
Coenen, M., & Rottensteiner, F. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.07.006 · Cited by 12
Exploring RGB-D Cameras for 3D Reconstruction of Cultural Heritage
Gomes, L., Silva, L., & Bellon, O. (2018) · ACM Journal on Computing and Cultural Heritage · doi:10.1145/3230674 · Cited by 8
A Data-Driven Regularization Model for Stereo and Flow
Wei, D., Liu, C., & Freeman, W. T. (2014) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2014.97 · Cited by 10
DCSLAM: A dynamically constrained real-time slam
Ramadasan, D., Chateau, T., & Chevaldonné, M. (2015) · International Conference on Information Photonics · doi:10.1109/icip.2015.7350976 · Cited by 13
Monocular Camera Based Real-Time Dense Mapping Using Generative Adversarial Network
Yang, X. Y., Chen, J., Wang, Z., Zhang, Q., Liu, W., Liao, C., & Cheng, K.-T. T. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240564 · Cited by 10
Tracking 6-DoF Object Motion from Events and Frames
Li, H., & Stueckler, J. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561760 · Cited by 11
RFFCE: Residual Feature Fusion and Confidence Evaluation Network for 6DoF Pose Estimation
Meng, Q., Ji, S., Zhu, S., Jin, T., Li, T., Gu, J. J., & Song, W. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160448 · Cited by 10
PRECISE VEHICLE RECONSTRUCTION FOR AUTONOMOUS DRIVING APPLICATIONS
Coenen, M., Rottensteiner, F., & Heipke, C. (2019) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-2-w5-21-2019 · Cited by 10
Joint 3D Reconstruction of a Static Scene and Moving Objects
Caccamo, S., Ataer-Cansızoğlu, E., & Taguchi, Y. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00082 · Cited by 10
Active 3D Shape Reconstruction from Vision and TouchPreprint
Smith, E. J., Meger, D., Pineda, L. E., Calandra, R., Malik, J., Romero, A., & Drozdzal, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.09584 · Cited by 10
Iterative closest point based 3D object reconstruction using RGB-D acquisition devices
Valgma, L., Daneshmand, M., & Anbarjafari, G. (2016) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2016.7495776 · Cited by 8
Semantic 3D Reconstruction of Heads
Maninchedda, F., Häne, C., Jacquet, B., Delaunoy, A., & Pollefeys, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_40 · Cited by 8
Probabilistic Vehicle Reconstruction Using a Multi-Task CNN
Coenen, M., & Rottensteiner, F. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00110 · Cited by 8
Non-linear Dimensionality Regularizer for Solving Inverse ProblemsPreprint
Garg, R., Eriksson, A. P., & Reid, I. D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.05015 · Cited by 8
Self-citationNeural Object Descriptors for Multi-View Shape ReconstructionPreprint
Sucar, E., Wada, K., & Davison, A. J. (2020) · arXiv (Cornell University) · Cited by 8
Real-time Monocular Dense Mapping for Augmented Reality
Xue, T., Luo, H., Cheng, D., Yuan, Z., & Yang, X. Y. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123348 · Cited by 7
A relative 3D scan and construction for face using meshing algorithm
Savakar, D. G., & Hosur, R. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5783-1 · Cited by 6
Ramadasan, D., Chevaldonné, M., & Château, T. (2015) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr.2015.7223398 · Cited by 6
Architectural decomposition for 3D landmark building understanding
Kobyshev, N., Riemenschneider, H., Bódis-Szomorú, A., & Gool, L. V. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477653 · Cited by 6
Non-Lambertian Surfaces and Their Challenges for Visual SLAM
Pyykölä, S., Joswig, N., & Ruotsalainen, L. (2024) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2024.3419832 · Cited by 5
Recognizing Scenes from Novel Viewpoints
Qian, S., Kirillov, A., Ravi, N., Chaplot, D. S., Johnson, J., Fouhey, D. F., & Gkioxari, G. (2021) · CaltechAUTHORS (California Institute of Technology) · doi:10.48550/arxiv.2112.01520 · Cited by 6
Metadata
- Authors
- Dame, A., Prisacariu, V. A., Ren, C. Y., & Reid, I.
- Affiliations
- University of Oxford (GB)
- The University of Adelaide (AU)
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 1288-1295
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2119493293
- Cited by
- 135
- References
- 28
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 3 · 2022: 4 · 2021: 10 · 2020: 16 · 2019: 12 · 2018: 16 · 2017: 23 · 2016: 18 · 2015: 19 · 2014: 11
- FWCI
- 28.25
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/H050795/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- dense reconstruction, monocular SLAM, shape priors, 3D object tracking, 6D pose estimation, depth sensors, object segmentation, 3D shape reconstruction, specular surfaces
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
