The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels
Open accessAbstract
Existing scene understanding datasets contain only a limited set of views of a place, and they lack representations of complete 3D spaces. In this paper, we introduce SUN3D, a large-scale RGB-D video database with camera pose and object labels, capturing the full 3D extent of many places. The tasks that go into constructing such a dataset are difficult in isolation -- hand-labeling videos is painstaking, and structure from motion (SfM) is unreliable for large spaces. But if we combine them together, we make the dataset construction task much easier. First, we introduce an intuitive labeling tool that uses a partial reconstruction to propagate labels from one frame to another. Then we use the object labels to fix errors in the reconstruction. For this, we introduce a generalization of bundle adjustment that incorporates object-to-object correspondences. This algorithm works by constraining points for the same object from different frames to lie inside a fixed-size bounding box, parameterized by its rotation and translation. The SUN3D database, the source code for the generalized bundle adjustment, and the web-based 3D annotation tool are all available at http://sun3d.cs.princeton.edu.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760
SUN database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539970 · Cited by 3,283
Self-citationFei-Fei, L., Fergus, R., & Perona, P. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2005.09.012 · Cited by 2,359
SLAM++: Simultaneous Localisation and Mapping at the Level of Objects
Salas-Moreno, R. F., Newcombe, R. A., Strasdat, H., Kelly, P. H. J., & Davison, A. J. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.178 · Cited by 945
Cited by
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T., & Niessner, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.261 · Cited by 4,368
Image segmentation using deep learning: A survey
Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3059968 · Cited by 3,796
Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., & Brox, T. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.438 · Cited by 2,488
SUN RGB-D: A RGB-D scene understanding benchmark suite
Song, S., Lichtenberg, S. P., & Xiao, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298655 · Cited by 1,943
Self-citationMatterport3D: Learning from RGB-D Data in Indoor Environments
Chang, A., Dai, A., Funkhouser, T., Halber, M., Niebner, M., Savva, M., Song, S., Zeng, A., & Zhang, Y. (2017) · 2017 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00081 · Cited by 1,858
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
Roberts, M., Ramapuram, J., Ranjan, A., Kumar, A., Bautista, M. A., Paczan, N., Webb, R., & Susskind, J. M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01073 · Cited by 358
Learning Depth with Convolutional Spatial Propagation Network
Cheng, X., Wang, P., & Yang, R. (2018) · arXiv · doi:10.48550/arxiv.1810.02695 · Cited by 345
Self-supervised model adaptation for multimodal semantic segmentation
Valada, A., Mohan, R., & Burgard, W. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01188-y · Cited by 293
3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics
Fu, H., Cai, B., Gao, L., Zhang, L.-X., Wang, J., Li, C., Zeng, Q., Sun, C., Jia, R., Zhao, B., et al. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01075 · Cited by 267
DeepTAM: Deep Tracking and Mapping
Zhou, H., Ummenhofer, B., & Brox, T. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01270-0_50 · Cited by 226
Monocular Relative Depth Perception with Web Stereo Data Supervision
Xian, K., Shen, C., Cao, Z., Lu, H., Xiao, Y., Li, R., & Luo, Z. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00040 · Cited by 215
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Mayer, N., Ilg, E., Fischer, P., Hazirbas, C., Cremers, D., Dosovitskiy, A., & Brox, T. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1082-6 · Cited by 191
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception
Wang, X., Zhu, Z., Xu, W., Zhang, Y., Wei, Y., Chi, X., Ye, Y., Du, D., Lu, J., & Wang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01636 · Cited by 192
Grasping in the Wild: Learning 6DoF Closed-Loop Grasping From Low-Cost Demonstrations
Song, S., Zeng, A., Lee, J., & Funkhouser, T. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3004787 · Cited by 185
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01834 · Cited by 182
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
“In our last experiment we demonstrate how CAM-Convs can generalize across datasests by training on four datasets with different cameras (KITTI [37], ScanNet[5], MegaDepth [23] and Sun3D[44] and testing on a different one (NYUv2 [31]).”
MethodA Cross-Season Correspondence Dataset for Robust Semantic Segmentation
Mans Larsson, M., Stenborg, E., Hammarstrand, L., Pollefeys, M., Sattler, T., & Kahl, F. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00976 · Cited by 71
Self-supervised 3D Representation Learning of Dressed Humans from Social Media Videos
Jafarian, Y., & Park, H. S. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3231558 · Cited by 12
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02557-6 · Cited by 3
Metadata
- Authors
- Xiao, J., Owens, A., & Torralba, A.
- Venue
- 2013 IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 1625-1632
- OpenAlex
- W1985238052
- Cited by
- 803
- References
- 5
- FWCI
- 74.48
- Type
- conference paper
- Language
- en
- Open access
- green
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