3D Room Layouts From A Single RGB Panorama

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-25.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (10 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Corners for Layout: End-to-End Layout Recovery from 360 Images
  19 Mar 2019 3 repositories listed Syntology ran 5 of 6 samples · 1 unverified · 6 pointer-only (licence)
  The problem of 3D layout recovery in indoor scenes has been a core research topic for over a decade.
• LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image
  23 Mar 2018 2 repositories listed Syntology ran 1 of 27 samples · 26 unverified
  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.
• 3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform
  19 Jul 2022 1 repository listed Syntology ran 7 of 12 samples · 5 unverified
  We transform the image feature from a cubemap tile to the Hough space of a Manhattan world and directly map the feature to the geometric output.
• LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering
  1 Apr 2021 1 repository listed Syntology ran 6 of 10 samples · 4 unverified
  Although significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in the 3D space.
• SSLayout360: Semi-Supervised Indoor Layout Estimation from 360-Degree Panorama
  25 Mar 2021 1 repository listed
  Recent years have seen flourishing research on both semi-supervised learning and 3D room layout reconstruction.
• HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features
  23 Nov 2020 1 repository listed
  We present HoHoNet, a versatile and efficient framework for holistic understanding of an indoor 360-degree panorama using a Latent Horizontal Feature (LHFeat).
• AtlantaNet: Inferring the 3D Indoor Layout from a Single 360(∘) Image beyond the Manhattan World Assumption
  1 Aug 2020 1 repository listed
  We introduce a novel end-to-end approach to predict a 3D room layout from a single panoramic image.
• HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation
  12 Jan 2019 1 repository listed
  We present a new approach to the problem of estimating the 3D room layout from a single panoramic image.
• DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama
  29 Nov 2018 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  We present a deep learning framework, called DuLa-Net, to predict Manhattan-world 3D room layouts from a single RGB panorama.

Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-25.