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Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
Open accessAbstract
For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. We address this challenge by introducing Hypersim, a photorealistic synthetic dataset for holistic indoor scene understanding. To create our dataset, we leverage a large repository of synthetic scenes created by professional artists, and we generate 77,400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry. Our dataset: (1) relies exclusively on publicly available 3D assets; (2) includes complete scene geometry, material information, and lighting information for every scene; (3) includes dense per-pixel semantic instance segmentations and complete camera information for every image; and (4) factors every image into diffuse reflectance, diffuse illumination, and a non-diffuse residual term that captures view-dependent lighting effects.We analyze our dataset at the level of scenes, objects, and pixels, and we analyze costs in terms of money, computation time, and annotation effort. Remarkably, we find that it is possible to generate our entire dataset from scratch, for roughly half the cost of training a popular open-source natural language processing model. We also evaluate sim-to-real transfer performance on two real-world scene understanding tasks – semantic segmentation and 3D shape prediction – where we find that pre-training on our dataset significantly improves performance on both tasks, and achieves state-of-the-art performance on the most challenging Pix3D test set. All of our rendered image data, as well as all the code we used to generate our dataset and perform our experiments, is available online.
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“VisGel [246], leveraging GelSight sensors, constructs bidirectional generation tasks between tactile and visual modalities, further validating the feasibility of inter-modal information transformation.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [246]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
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
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
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
CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718
OctoMap: an efficient probabilistic 3D mapping framework based on octrees
Hornung, A., Wurm, K. M., Bennewitz, M., Stachniss, C., & Burgard, W. (2013) · Autonomous Robots · doi:10.1007/s10514-012-9321-0 · Cited by 2,877
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., & Dollár, P. (2014) · arXiv · doi:10.48550/arxiv.1405.0312 · Cited by 2,579
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.352 · Cited by 2,338
ShapeNet: An Information-Rich 3D Model Repository
Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. (2015) · arXiv · doi:10.48550/arxiv.1512.03012 · Cited by 2,330
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
Shah, S., Dey, D., Lovett, C., & Kapoor, A. (2018) · Springer Proceedings in Advanced Robotics · doi:10.1007/978-3-319-67361-5_40 · Cited by 2,254
Playing for Data: Ground Truth from Computer Games
Richter, S. R., Vineet, V., Roth, S., & Koltun, V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46475-6_7 · Cited by 1,981
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
3D Semantic Parsing of Large-Scale Indoor Spaces
Armeni, I., Şener, O., Zamir, A., Jiang, H., Brilakis, I., Fischer, M. A., & Savarese, S. (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.2016.170 · Cited by 1,897
Matterport3D: 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
Semantic Understanding of Scenes Through the ADE20K Dataset
Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., & Torralba, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1140-0 · Cited by 1,831
ShapeNet: An Information-Rich 3D Model Repository
Chang, A. X., Funkhouser, T. A., Guibas, L. J., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. (2023) · doi:10.5281/zenodo.10089705 · Cited by 1,774
Semantic Scene Completion from a Single Depth Image
Song, S., Yu, F., Zeng, A., Chang, A. X., Savva, M., & Funkhouser, T. 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.2017.28 · Cited by 1,244
Habitat: A Platform for Embodied AI Research
Savva, M., Kadian, A., Maksymets, O., Zhao, Y., Wijmans, E., Jain, B., Straub, J., Liu, J., Koltun, V., Malik, J., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00943 · Cited by 1,237
Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sunderhauf, N., Reid, I., Gould, S., & van den Hengel, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00387 · Cited by 1,187
Settles, B. (2012) · Cited by 1,183
VirtualWorlds as Proxy for Multi-object Tracking Analysis
Gaidon, A., Wang, Q., Cabon, Y., & Vig, E. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.470 · Cited by 1,079
Joint 2D-3D-Semantic Data for Indoor Scene UnderstandingPreprint
Armeni, I., (Sasha), A. S., Zamir, A., & Savarese, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.01105 · Cited by 929
A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM
Handa, A., Whelan, T. J., McDonald, J., & Davison, A. J. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907054 · Cited by 907
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., & Catanzaro, B. (2019) · arXiv · doi:10.48550/arxiv.1909.08053 · Cited by 893
Akenine‐Möller, T., Haines, E., Hoffman, N., Pesce, A., Iwanicki, M., & Hillaire, S. (2018) · A K Peters/CRC Press eBooks · doi:10.1201/b22086 · Cited by 878
Level of Detail for 3D Graphics
Luebke, D. P., Reddy, M., Cohen, J. D., Varshney, A., Watson, B. A., & Huebner, R. J. (2012) · Cited by 827
SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels
Xiao, J., Owens, A., & Torralba, A. B. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.458 · Cited by 805
Gibson Env: Real-World Perception for Embodied Agents
Xia, F., Zamir, A. R., He, Z., Sax, A., Malik, J., & Savarese, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00945 · Cited by 763
Beyond PASCAL: A benchmark for 3D object detection in the wild
Yu, X., Mottaghi, R., & Savarese, S. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836101 · Cited by 741
The Replica Dataset: A Digital Replica of Indoor SpacesPreprint
Straub, J., Whelan, T. J., Ma, L., Chen, Y., Wijmans, E., Green, S., Engel, J. J., Mur-Artal, R., Ren, C. Y., Verma, S., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05797 · Cited by 711
Shape, Illumination, and Reflectance from Shading
Barron, J. T., & Malik, J. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2377712 · Cited by 660
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images
Gupta, S., Arbeláez, P., & Malik, J. (2013) · 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.2013.79 · Cited by 603
AI2-THOR: An Interactive 3D Environment for Visual AI
Kolve, E., Mottaghi, R., Han, W., VanderBilt, E., Weihs, L., Herrasti, A., Deitke, M., Ehsani, K., Gordon, D., Zhu, Y., et al. (2017) · arXiv · doi:10.48550/arxiv.1712.05474 · Cited by 578
ScanNet: Richly-annotated 3D Reconstructions of Indoor ScenesPreprint
Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T. A., & Nießner, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.04405 · Cited by 537
High Quality Monocular Depth Estimation via Transfer LearningPreprint
Alhashim, I., & Wonka, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.11941 · Cited by 524
Settles, B. (2012) · Synthesis lectures on artificial intelligence and machine learning · doi:10.1007/978-3-031-01560-1 · Cited by 503
Indoor Semantic Segmentation using depth information
Couprie, C., Farabet, C., Najman, L. A., & LeCun, Y. (2013) · HAL · doi:10.48550/arxiv.1301.3572 · Cited by 486
Gkioxari, G., Johnson, J., & Malik, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00988 · Cited by 486
Synthetic Data for Deep Learning
Nikolenko, S. (2021) · Springer optimization and its applications · doi:10.1007/978-3-030-75178-4 · Cited by 474
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
Sun, X., Wu, J., Zhang, X., Zhang, Z., Zhang, C., Xue, T., Tenenbaum, J. B., & Freeman, W. T. (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.2018.00314 · Cited by 456
TartanAir: A Dataset to Push the Limits of Visual SLAM
Wang, W., Zhu, D., Wang, X., Hu, Y., Qiu, Y., Wang, C., Hu, Y., Kapoor, A., & Scherer, S. (2020) · 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341801 · Cited by 456
Richter, S. R., Hayder, Z., & Koltun, V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.243 · Cited by 451
Bell, S., Bala, K., & Snavely, N. (2014) · ACM Transactions on Graphics · doi:10.1145/2601097.2601206 · Cited by 403
SceneNN: A Scene Meshes Dataset with aNNotations
Hua, B., Pham, Q.-H., Nguyen, D. T., Tran, M.-K., Yu, L.-F., & Yeung, S.-K. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.18 · Cited by 347
Which Tasks Should Be Learned Together in Multi-task Learning?Preprint
Standley, T., Zamir, A., Chen, D., Guibas, L. J., Malik, J., & Savarese, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.07553 · Cited by 330
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Wu, Y., Wu, Y., Gkioxari, G., & Tian, Y. (2018) · CaltechAUTHORS (California Institute of Technology) · doi:10.48550/arxiv.1801.02209 · Cited by 326
DIODE: A Dense Indoor and Outdoor DEpth DatasetPreprint
Vasiljevic, I., Kolkin, N., Zhang, S., Luo, R., Wang, H., Dai, F. Z., Daniele, A. F., Mostajabi, M., Basart, S., Walter, M. R., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.00463 · Cited by 300
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-training on Indoor Segmentation?
McCormac, J., Handa, A., Leutenegger, S., & Davison, A. J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.292 · Cited by 291
Efficiently Approximating the Minimum-Volume Bounding Box of a Point Set in Three Dimensions
Barequet, G., & Har-Peled, S. (2001) · Journal of Algorithms · doi:10.1006/jagm.2000.1127 · Cited by 289
Structured3D: A Large Photo-Realistic Dataset for Structured 3D Modeling
Zheng, J., Zhang, J., Li, J., Tang, R., Gao, S., & Zhou, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_30 · Cited by 286
Li, Z., Shafiei, M., Ramamoorthi, R., Sunkavalli, K., & Chandraker, M. K. (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/cvpr42600.2020.00255 · Cited by 265
MINOS: Multimodal Indoor Simulator for Navigation in Complex EnvironmentsPreprint
Savva, M., Chang, A. X., Dosovitskiy, A., Funkhouser, T., & Koltun, V. (2017) · arXiv.org · doi:10.48550/arxiv.1712.03931 · Cited by 262
3D-FUTURE: 3D Furniture Shape with TextURE
Fu, H., Jia, R., Gao, L., Gong, M., Zhao, B., Maybank, S., & Tao, D. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01534-z · Cited by 254
Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks
Zhang, Y., Song, S., Yumer, E., Savva, M., Lee, J., Jin, H., & Funkhouser, T. 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.2017.537 · Cited by 251
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Lim, J. J., Pirsiavash, H., & Torralba, A. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.372 · Cited by 249
Understanding RealWorld Indoor Scenes with Synthetic Data
Handa, A., Pătrăucean, V., Badrinarayanan, V., Stent, S., & Cipolla, R. (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.2016.442 · Cited by 248
Learning Non-Lambertian Object Intrinsics Across ShapeNet Categories
Shi, J., Dong, Y., Su, H., & Yu, S. X. (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.2017.619 · Cited by 188
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Wrenninge, M., & Unger, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.08705 · Cited by 186
InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes DatasetPreprint
Li, W., Sajad Saeedi, G., McCormac, J., Clark, R., Tzoumanikas, D., Ye, Q., Huang, Y., Tang, R., & Leutenegger, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.00716 · Cited by 171
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Saleh, F. S., Aliakbarian, M. S., Salzmann, M., Petersson, L., & Álvarez, J. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_6 · Cited by 169
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Nikolenko, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11512 · Cited by 168
CGIntrinsics: Better Intrinsic Image Decomposition Through Physically-Based Rendering
Li, Z., & Snavely, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_23 · Cited by 158
Neural Inverse Rendering of an Indoor Scene From a Single Image
Sengupta, S., Gu, J., Kim, K., Liu, G., Jacobs, D. W., & Kautz, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00869 · Cited by 151
Isenburg, M., & Lindström, P. (2005) · Cited by 148
Li, K., & Malik, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46475-6_42 · Cited by 147
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
Hurl, B., Czarnecki, K., & Waslander, S. (2019) · 2019 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2019.8813809 · Cited by 122
SceneNet: An annotated model generator for indoor scene understanding
Handa, A., Pătrăucean, V., Stent, S., & Cipolla, R. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487797 · Cited by 109
Intrinsic Decompositions for Image Editing
Bonneel, N., Kovács, B., Paris, S., & Bala, K. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13149 · Cited by 80
Free Supervision from Video Games
Krähenbühl, P. (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.2018.00312 · Cited by 80
Jiang, C., Qi, S., Zhu, Y., Huang, S., Lin, J., Yu, L.-F., Terzopoulos, D., & Zhu, S. C. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1103-5 · Cited by 80
Shading Annotations in the Wild
Kovács, B., Bell, S., Snavely, N., & Bala, K. (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.2017.97 · Cited by 69
A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation
Nguyen, D. T., Hua, B., Yu, L.-F., & Yeung, S.-K. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2772238 · Cited by 47
García-García, A., Martínez-González, P., Oprea, S., Castro-Vargas, J. A., Orts‐Escolano, S., García‐Rodríguez, J., & Jover-Álvarez, Á. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594495 · Cited by 43
Khan, S., Phan, B., Salay, R., & Czarnecki, K. (2019) · Cited by 39
Structured3D: A Large Photo-realistic Dataset for Structured 3D ModelingPreprint
Zheng, J., Zhang, J., Li, J., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.00222 · Cited by 26
TartanAir: A Dataset to Push the Limits of Visual SLAMPreprint
Wang, W., Zhu, D., Wang, X., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.14338 · Cited by 25
COMPUTING THE DIAMETER OF A POINT SET
Malandain, G., & Boissonnat, J. (2002) · International Journal of Computational Geometry & Applications · doi:10.1142/s0218195902001006 · Cited by 24
Unlimited Road-scene Synthetic Annotation (URSA) Dataset
Angus, M., ElBalkini, M., Khan, S., Harakeh, A., Andrienko, O., Reading, C., Waslander, S. L., & Czarnecki, K. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569519 · Cited by 23
(2019) · arXiv (Cornell University) · Cited by 16
3D-FUTURE: 3D Furniture shape with TextUREPreprint
Fu, H., Jia, R., Gao, L., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.09633 · Cited by 15
Learning Where to Look: Data-Driven Viewpoint Set Selection for 3D ScenesPreprint
Genova, K., Savva, M., Chang, A. X., & Funkhouser, T. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.02393 · Cited by 13
OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene DatasetsPreprint
Li, Z., Yu, T.-W., Sang, S., Wang, S., Song, M., Liu, Y., Yeh, Y.-Y., Zhu, R., Gundavarapu, N., Shi, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.12868 · Cited by 11
Liu, B., Zhang, J., Zhang, X., Zhang, W., Yu, C., & Zhou, Y. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.09299 · Cited by 10
Research works printed but not linked: 2
- jin, 0, Geometric structure based and regularized depth estimation from 360 degree indoor imagery, CVPR 2020
- xiang, 0, A large scale database for 3D object recognition, ECC 2016
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Metadata
- Authors
- Roberts, M., Ramapuram, J., Ranjan, A., Kumar, A., Bautista, M. A., Paczan, N., Webb, R., & Susskind, J. M.
- Affiliations
- Apple (United States) (US)
- Apple
- ORCID
- Ramapuram, Jason
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 10892-10902
- OpenAlex
- W3097660860
- Cited by
- 358
- References
- 95
- Citations by year
- 2026: 85 · 2025: 88 · 2024: 73 · 2023: 61 · 2022: 31 · 2021: 19
- FWCI
- 33.65
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IEEE/CVF International Conference on Computer Vision (ICCV)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- synthetic dataset, indoor scene understanding, photorealistic rendering, instance segmentation, sim-to-real transfer, diffuse reflectance, scene geometry, annotation cost
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 ›
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