The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image
Open accessAbstract
We propose an algorithm to predict room layout from a single image that generalizes across panoramas and perspective images, cuboid layouts and more general layouts (e.g. "L"-shape room). Our method operates directly on the panoramic image, rather than decomposing into perspective images as do recent works. Our network architecture is similar to that of RoomNet [15], but we show improvements due to aligning the image based on vanishing points, predicting multiple layout elements (corners, boundaries, size and translation), and fitting a constrained Manhattan layout to the resulting predictions. Our method compares well in speed and accuracy to other existing work on panoramas, achieves among the best accuracy for perspective images, and can handle both cuboid-shaped and more general Manhattan layouts.
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.
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
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
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
Self-citationAlgorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
Zhu, C., Byrd, R. H., Lu, P., & Nocedal, J. (1997) · ACM Transactions on Mathematical Software · doi:10.1145/279232.279236 · Cited by 3,462
LSD: A Fast Line Segment Detector with a False Detection Control
von Gioi, R. G., Jakubowicz, J., Morel, J.-M., & Randall, G. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.300 · Cited by 1,946
Geometric context from a single image
Hoiem, D., Efros, A. A., & Hebert, M. (2005) · Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 · doi:10.1109/iccv.2005.107 · Cited by 662
Self-citationJoint 2D-3D-Semantic Data for Indoor Scene Understanding
Armeni, I., (Sasha), A. S., Zamir, A., & Savarese, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.01105 · Cited by 621
Recovering the spatial layout of cluttered rooms
Hedau, V., Hoiem, D., & Forsyth, D. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459411 · Cited by 487
Self-citationManhattan World: compass direction from a single image by Bayesian inference
Coughlan, J. M., & Yuille, A. L. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790349 · Cited by 475
Cited by
MonoScene: Monocular 3D Semantic Scene Completion
Cao, A.-Q., & de Charette, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00396 · Cited by 293
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 282
BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud
Ng, M. H., Radia, K., Chen, J., Wang, D., Gog, I., & Gonzalez, J. E. (2020) · arXiv · doi:10.48550/arxiv.2006.11436 · Cited by 40
Plan2Scene: Converting Floorplans to 3D Scenes
Vidanapathirana, M., Wu, Q., Furukawa, Y., Chang, A. X., & Savva, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01059 · Cited by 32
Metadata
- Authors
- Zou, C., Colburn, A., Shan, Q., & Hoiem, D.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 2051-2059
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2962717701
- Cited by
- 257
- References
- 32
- FWCI
- 52.11
- Type
- conference paper
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image. https://app.cochat.ai/papers/10.1109/cvpr.2018.00219
- The paper
- Zou, C., Colburn, A., Shan, Q., & Hoiem, D. (2018). LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00219
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
