The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Understanding RealWorld Indoor Scenes with Synthetic Data
Open accessAbstract
Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments. Recent attempts with supervised learning have shown promise in this direction but also highlighted the need for enormous quantity of supervised data- performance increases in proportion to the amount of data used. However, this quickly becomes prohibitive when considering the manual labour needed to collect such data. In this work, we focus our attention on depth based semantic per-pixel labelling as a scene understanding problem and show the potential of computer graphics to generate virtually unlimited labelled data from synthetic 3D scenes. By carefully synthesizing training data with appropriate noise models we show comparable performance to state-of-the-art RGBD systems on NYUv2 dataset despite using only depth data as input and set a benchmark on depth-based segmentation on SUN RGB-D dataset.
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Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471
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
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
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SUN RGB-D: A RGB-D scene understanding benchmark suite
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Cited by
A research agenda for augmented and virtual reality in architecture, engineering and construction
Davila Delgado, J. M., Oyedele, L., Demian, P., & Beach, T. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101122 · Cited by 486
BlendedMVS: A Large-Scale Dataset for Generalized Multi-View Stereo Networks
Yao, Y., Luo, Z., Li, S., Zhang, J., Ren, Y., Zhou, L., Fang, T., & Quan, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00186 · Cited by 478
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456
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
3DMV: Joint 3D-Multi-view Prediction for 3D Semantic Scene Segmentation
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Metadata
- Authors
- Handa, A., Patraucean, V., Badrinarayanan, V., Stent, S., & Cipolla, R.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 4077-4085
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2341569833
- Cited by
- 230
- References
- 8
- FWCI
- 9.63
- Type
- conference paper
- Language
- en
- Open access
- green
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- Handa, A., Patraucean, V., Badrinarayanan, V., Stent, S., & Cipolla, R. (2016). Understanding RealWorld Indoor Scenes with Synthetic Data. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.442
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