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.
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“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]
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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
Flagged first, then by citations
3 of the 28 references couldn’t be matched to a known paper.
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++
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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
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
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