Floor-SP: Inverse CAD for Floorplans by Sequential Room-wise Shortest Path

Chen Liu; Jiacheng Chen; Jiaye Wu; Yasutaka Furukawa

REVIEW 3 major objections 4 minor 30 references

Floor-SP: Inverse CAD for Floorplans by Sequential Room-wise Shortest Path

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A floorplan can be reconstructed from RGBD scans as a set of room loops by solving one shortest-path problem per room, and the paper shows this outperforms the prior state of the art.

desk verdict New formulation and dataset, but the headline FloorNet comparison may mix benchmarks and needs verification before the main claim is accepted. read the letter →

arxiv 1908.06702 v1 pith:P3REAOBJ submitted 2019-08-19 cs.CV

Jiacheng Chen , Chen Liu , Jiaye Wu , Yasutaka Furukawa This is my paper · ORCID

classification cs.CV

keywords floorplanreconstructionRGBDscansroom-wisecoordinatedescentshortestpathenergyminimizationinstancesegmentationnon-Manhattanfloorplansgraph

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Floor-SP argues that floorplan reconstruction from aligned RGBD scans can be solved as an optimization problem over one polygonal loop per room, instead of the usual bottom-up detect-then-connect pipeline. The objective mixes learned per-pixel corner and wall costs, a penalty for loops that cut through room masks, a consistency term that rewards neighboring rooms sharing corners and walls, and a complexity term that prefers fewer corners. The optimization visits rooms one at a time and solves a shortest-path problem for each, keeping already reconstructed neighbors fixed. The paper reports that this scheme reconstructs rooms with precision/recall 84.7/83.0 and Room++ 51.4/50.4 on 94 test apartments, beating FloorNet's 81.2/72.1 and 42.3/37.5, and that it handles non-Manhattan structures by allowing each room its own subset of dominant directions. A reader would care because this offers a way to avoid error accumulation from early corner and edge detection while still producing complete room loops in challenging scans.

What carries the argument

The load-bearing object is the pixel-graph shortest-path reduction. Every pixel is a node; an edge between pixels carries a weight that collects one half of the corner cost at its two endpoints, the learned edge cost and room-mask penalty along the Bresenham line, a consistency discount that rewards pixels already used by other loops, and a fixed per-corner complexity charge. Fixing a start-edge and forbidding crossings of a perpendicular start-line enforces that the loop surrounds its room mask. Room-wise coordinate descent visits rooms in increasing order of area and solves this shortest-path problem for each, then repeats a second round to fix overlaps. A dominant-direction precomputation restricts candidate edges to a subset of global Manhattan frames, which is how the system extends beyond a single global Manhattan assumption.

What would settle it

Take a test scan and merge two adjacent rooms into a single mask by editing the Mask R-CNN output while keeping all sensor inputs identical, then rerun Floor-SP. The paper's Section 7.3 discussion predicts the final graph will contain one combined room with an incorrect shared-wall topology, because the energy has no term that can split a room proposal; observing a recovered two-room graph would refute the claimed dependence on room-count correctness.

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Extended reading notes

Core claim

The central claim is that wall-graph inference for floorplans reduces to joint energy minimization over room loops once room segments are available. Each room's loop is a closed pixel sequence with an unknown number of corners; the paper defines data terms from DNN corner and edge likelihood maps plus a room-mask interior penalty, a consistency term over shared corner and edge pixels across all loops, and a model-complexity term counting corners. Minimizing this objective by room-wise coordinate descent, where each step is a shortest-path problem on a pixel graph with edge weights derived from the energy, yields the floorplan graph after simple loop merging. The paper's evidence is the quantitative comparison on 527 production-quality panorama RGBD scans, with 94 held out for testing: room precision/recall rises to 84.7/83.0 and Room++ to 51.4/50.4, against 81.2/72.1 and 42.3/37.5 for the prior system FloorNet.

Load-bearing premise

The load-bearing premise, conceded in the paper's Section 7.3 discussion, is that the initial room-segmentation step already determined the correct number of rooms and a roughly correct mask for each one; if a room is missed or two rooms are merged into one segment, no later stage can split or create rooms, so the final floorplan topology is wrong regardless of how well the shortest-path optimization runs.

Editorial extensions

If this is right

If Floor-SP's central claim is right, floorplan reconstruction no longer has to commit to thresholded corner and edge detections; an optimizer can place and connect corners from learned cost maps.
Because every room proposal is guaranteed a loop, room-level recall no longer collapses when a corner detector fires weakly on occluded walls, which is the mechanism behind the recall gains over FloorNet.
The ablation table in the paper shows that removing the consistency term lowers Room++ precision/recall from 51.4/50.4 to 49.4/48.5, so shared-wall agreement is a source of the gain rather than an artifact of post-processing.
The dominant-direction approximation means non-Manhattan rooms do not need one global Manhattan frame; each room can pick its own subset of up to four frames, which is why the system reports strong results on the non-Manhattan rooms in the test set.
Two rounds of coordinate descent can repair overlaps and holes that a single round leaves, which is why the paper fixes the optimization to two rounds.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct extension the paper leaves implicit is to let the optimizer correct the room count, for example by generating candidate loops from the corner and edge likelihood maps and accepting or rejecting them under the same energy; that would attack the dominant failure mode identified in Section 7.3.
The coordinate-descent plus shortest-path pattern is not tied to floorplans; any reconstruction task whose output is a set of closed contours with learned per-pixel costs could reuse the reduction, such as building footprints, cadastral parcels, or cell boundaries in imagery.
Because the paper notes that high-resolution panoramas and full 3D points are discarded, an untested extension is to feed those signals into the data terms; the reported corner-recall gap in occluded areas suggests this is where the next gain would come from.
A testable prediction following from the paper's own failure analysis: on the subset of test units where the initial room segmentation has the correct number of rooms and roughly correct masks, room-level metrics should be near the top of the table, with almost all residual failures confined to non-Manhattan geometry and segmentation-count errors.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Floor-SP proposes a floorplan reconstruction system that starts from Mask R-CNN room segments and solves a joint energy over multiple polygonal loops using room-wise coordinate descent, with each step reduced to a shortest-path problem. That formulation is new relative to the cited prior work, and the 527-scan production-quality dataset with a 94-scan test split is a real resource. The paper is well written, the ablations are informative, and the failure analysis in 7.3 is honest: they own the fact that a wrong room count from Mask R-CNN is unrecoverable.

The problem is Table 1. The FloorNet row appears to be taken from the original FloorNet paper, which evaluated on 155 smartphone RGBD video scans. Floor-SP is evaluated on 94 panorama scans from a different 527-unit set, with different sensors and annotation protocols. The paper never says FloorNet was retrained or rerun on the same split. If it was not, the reported gains (Room++ 51.4/50.4 vs 42.3/37.5) are not a comparison of algorithms; they are a comparison of datasets. This is the load-bearing claim of the paper, so the omission matters more than the other issues.

The rest of the concerns are secondary but real: no error bars or paired per-unit analysis, a single baseline, one train/test split, a heuristic containment step that the authors acknowledge breaks local optimality, and a fixed two rounds of coordinate descent with no convergence analysis. The room-segmentation limitation is a true limitation but reasonably scoped.

I think the reader's overall verdict of accept is too generous given the measurement gap. The stress-test note is on target. The paper deserves a serious referee, but the editor should send it back with the explicit request that the authors either rerun FloorNet on their test split or substantially soften the comparison and rework the text. If the numbers are confirmed, this is a solid contribution; if not, the novelty of the formulation and dataset still makes it publishable, but the headline would have to change.

Referee Report

3 major / 4 minor

Summary. Floor-SP reconstructs a floorplan from aligned panorama RGBD scans as multiple polygonal loops, one per room, by minimizing an energy that combines DNN-based corner/edge data terms, a consistency term encouraging rooms to share corners and walls, and a model-complexity penalty. The optimization is performed by room-wise coordinate descent, where each step is reduced to a shortest-path problem on a pixel graph. The paper evaluates the approach on 527 production-quality panorama scans (433 training, 94 test), deliberately making the test set challenging with 199 of 667 rooms non-Manhattan, and reports substantial improvements over FloorNet on Room and Room++ precision/recall metrics. Ablation experiments show the contribution of the data term, consistency term, and multiple coordinate-descent rounds.

Significance. If the reported comparison is valid, Floor-SP is a meaningful advance for floorplan reconstruction, particularly for non-Manhattan scenes, which previous optimization-based approaches handled poorly. The formulation is elegant: reducing multi-loop floorplan inference to a sequence of shortest-path problems with data and consistency terms is a creative and potentially influential idea. The release of code and data on the project website is a concrete strength that enables reproducibility and follow-up work. However, the central claim of a significant performance boost over the state of the art is currently undercut by a potentially uncontrolled comparison: the FloorNet baseline in Table 1 may be taken from the original FloorNet paper's 155-unit smartphone-video benchmark rather than re-evaluated on the same 94-unit panorama test split. If so, the reported gains are confounded by dataset, sensor, annotation protocol, and difficulty differences. The paper also explicitly acknowledges that room instance segmentation errors on the number of rooms cannot be recovered, which limits the method's robustness but is at least stated in Section 7.3.

major comments (3)

[Section 7.2, Table 1] The headline comparison against FloorNet appears to mix benchmarks. The paper states that FloorNet was evaluated on 155 residential units with smartphone RGBD videos (Section 2), while Section 7 evaluates Floor-SP on a deliberately harder test split of 94 panorama scans with 199 of 667 non-Manhattan rooms. The text in Section 7.2 only says 'We follow FloorNet [20] and define metrics'; it never states that FloorNet was retrained or re-run on the same 94-unit test set, nor how FloorNet's video-based pipeline was adapted to the 4-channel top-down input. If the FloorNet row in Table 1 is the original published numbers, then the reported gains (Room 84.7/83.0 vs. 81.2/72.1; Room++ 51.4/50.4 vs. 42.3/37.5) compare different datasets, sensors, and difficulty levels, and do not support the central claim of a significant performance boost. Please clarify the experimental setup and, if the numbers are not from a matched evaluation, re-run a comparable baseline on the same test split or temper the claim accordingly.
[Section 5, 'Containment constraint satisfaction'] The heuristic that fixes a start-edge and removes all intersecting edges to enforce loop containment breaks the local optimality of the coordinate-descent step, as the paper acknowledges. This means the reported energy minimization is not an exact solver of the stated objective; the claim that Floor-SP 'solves a shortest path problem to optimize the room structure' is therefore qualified. Since the start-edge selection depends on a corner candidates extracted from the likelihood map, the paper should at least analyze sensitivity to this choice (e.g., how often the selected start-edge is a true wall segment) or discuss the impact on the final energy. Without such analysis, the significance of the 'optimization' contribution relative to the prior heuristic system [17] is unclear.
[Section 5, 'Sequential room-wise shortest path'] The coordinate descent is run for exactly two rounds, with no convergence criterion and no analysis of whether two rounds suffice. The claim that 'room-wise coordinate descent adds a further boost' is based on a single fixed number of iterations, and Figure 7 shows only two energy values. The paper should either run the descent to convergence and report the energy trajectory, or justify the two-round choice with evidence that additional rounds do not change the results. This is directly related to the central claim that the method optimizes the energy rather than merely applying a fixed number of heuristic passes.

minor comments (4)

[Section 7.2, Table 1] No error bars, variance estimates, or per-unit paired statistics are reported for any metric in Table 1. Given that the reported Room++ differences are roughly 9 percentage points, the paper would be much stronger with confidence intervals or a paired analysis over the 94 test units to show that the differences are not driven by a few outliers.
[Abstract and Section 1] The claim that the approach 'does not require corner/edge detection with thresholds' is overstated. The system uses a DRN to estimate corner and edge likelihoods, then extracts corner candidates and a start-edge using those maps (Section 5), and Mask R-CNN with a lowered detection threshold (Section 6). While the method avoids explicit threshold-based corner/edge detection as a final representation, threshold-like decisions are present in the pipeline.
[Figure 7] The figure reports energy values after each coordinate-descent round, but the y-axis is not labeled and the absolute scale of the energy is not defined. Please clarify what energy is plotted (the total objective from Section 4?) and include units or a scale so the reader can interpret the claimed improvement.
[Section 7.3] The paper should explicitly state in the conclusion that the method inherits the room-instance-segmentation errors and cannot split or create rooms, as it already does in the discussion. This is a known limitation, but it also means that the reported room-level metrics are partly determined by Mask R-CNN performance, so the paper should cite the Mask R-CNN room detection accuracy (e.g., how often the number of rooms is correct) to help the reader disentangle the contribution of the proposed optimization.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the room-wise shortest-path derivation and held-out evaluation are self-contained.

full rationale

The paper's derivation chain is self-contained. Section 4 defines a multi-loop energy as a sum of DNN-derived unary data terms, a consistency term, and a corner-count complexity term. Section 5 shows a direct algebraic reduction of this energy to shortest-path edge weights: the weights are exactly the pixel-wise costs plus lambda6 per corner, with the consistency terms rewritten as one-minus-indicator costs. No equation is identical to its input by construction; the containment constraint and EI_data penalty use the Mask R-CNN room segments as an upstream proposal, but the output loop is optimized over sensor-data likelihoods, and the paper explicitly acknowledges in Section 7.3 that errors in the number of room segments cannot be recovered. The DNN data terms are trained on 433 units and evaluated on a held-out 94-unit split, so room-level precision/recall is not a fitted quantity. Self-citations to [2], [17], and [20] provide conceptual inspiration and a baseline, but none is load-bearing: the shortest-path reduction, the coordinate-descent scheduling, and the consistency term are derived in the paper rather than imported. The FloorNet comparison in Table 1 may raise a benchmark-comparability concern, but that is an experimental-control issue, not a circularity of the derivation.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central claim rests on six manually chosen or ad hoc parameters, notably the energy weights, Mask R-CNN threshold, and dominant-direction frame count; a strong reliance on room segmentation correctness; and the restriction to a few dominant wall directions. No new physical entities are introduced.

free parameters (6)

Energy weights λ1-λ6 = λ1=0.2, λ2=0.2, λ3=100.0, λ4=0.2, λ5=0.1, λ6=1.0
Manually set scalars in Section 4; directly control corner/edge data cost, interior penalty, consistency, and model complexity.
Mask R-CNN detection threshold = 0.2 (lowered from default 0.7)
Section 6; determines the set of room proposals, a load-bearing input to all later stages.
Room mask erosion iterations = 2 (8-connected)
Section 6; shrinks room masks and alters where the interior penalty term applies.
SPA bounding box dilation and margin = 10 dilation iterations, 5-pixel margin
Section 5; restricts the shortest-path domain and therefore which wall candidates are reachable.
Number of dominant direction frames = 4 frames (8 directions)
Section 5; restricts allowed edge directions per room; walls outside these directions cannot be reconstructed.
Coordinate descent rounds = 2
Section 5; no convergence criterion, so the number of rounds is a hand-chosen stopping rule.

assumptions (6)

domain assumption Floorplan boundaries can be represented as polygonal loops with pixel-aligned corners and straight edges.
Section 4 defines Li as polygonal pixel loops; curved or free-form walls are out of scope.
domain assumption The room segmentation provides the correct room partition (count and rough shape).
Section 3 and failure discussion in Section 7.3; unrecoverable if wrong.
domain assumption All wall directions can be captured by four Manhattan frames extracted globally and assigned per room.
Section 5 dominant direction approximation; non-Manhattan rooms with other angles are impossible to recover (Section 7.3).
ad hoc to paper The start-line constraint enforces the containment of the room mask well enough.
Section 5; authors note fixing the start-edge breaks local optimality, and details are deferred to the supplementary document.
standard math Dijkstra's algorithm with non-negative edge weights solves every coordinate-descent subproblem exactly.
Section 5; the reduction to a shortest-path problem assumes edge weights are non-negative and Dijkstra returns the optimum.
domain assumption DNN likelihood models trained on the same dataset generalize to the test scans.
Section 6 describes supervised training of Mask R-CNN and DRN on the training split; test performance assumes distributional match.

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Cite this review

Pith. "Pith review of Floor-SP: Inverse CAD for Floorplans by Sequential Room-wise Shortest Path." pith.science (2026). https://pith.science/paper/P3REAOBJ

@misc{pith2026190806702,
  author       = {Pith},
  title        = {Pith review of: Floor-SP: Inverse CAD for Floorplans by Sequential Room-wise Shortest Path},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/P3REAOBJ}},
  note         = {Machine review of arXiv:1908.06702}
}

read the original abstract

This paper proposes a new approach for automated floorplan reconstruction from RGBD scans, a major milestone in indoor mapping research. The approach, dubbed Floor-SP, formulates a novel optimization problem, where room-wise coordinate descent sequentially solves dynamic programming to optimize the floorplan graph structure. The objective function consists of data terms guided by deep neural networks, consistency terms encouraging adjacent rooms to share corners and walls, and the model complexity term. The approach does not require corner/edge detection with thresholds, unlike most other methods. We have evaluated our system on production-quality RGBD scans of 527 apartments or houses, including many units with non-Manhattan structures. Qualitative and quantitative evaluations demonstrate a significant performance boost over the current state-of-the-art. Please refer to our project website http://jcchen.me/floor-sp/ for code and data.

Figures

Figures reproduced from arXiv: 1908.06702 by the authors.

**Figure 1.** The proposed system, dubbed Floor-SP, takes aligned panorama RGBD scans as input, finds room segments, solves an optimization problem to reconstruct a floorplan graph as multiple polygonal loops (one for each room), and merges them into a 2D graph via simple post-processing heuristics. The optimization is the technical contribution of the paper, which employs the room-wise coordinate descent strategy and sequential… view at source ↗

**Figure 2.** System overview: (Left) Mask-RCNN finds room segments (raster) from a top-down projection image consisting of point density [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Illustration of data and consistency terms. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: We solve a shortest path problem for each room, where [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Our dataset offers production-level panorama RGBD scans for 527 houses/apartments. We convert each scan into a point [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: Qualitative comparisons against FloorNet [20] and the variants of our approach. We select hard non-Manhattan examples [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

**Figure 9.** Figure 9: Standard corner detection easily makes mistakes ( [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]

**Figure 8.** Figure 8: Typical failure modes. The top is the ground-truth an [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

30 extracted references · 30 canonical work pages

[20]

Floornet: A uniﬁed framework for ﬂoorplan reconstruction from 3d scans

Chen Liu, Jiaye Wu, and Yasutaka Furukawa. Floornet: A uniﬁed framework for ﬂoorplan reconstruction from 3d scans. In European Conference on Computer Vision (ECCV), 2018

work page 2018
[17]

Struc- tured indoor modeling

Satoshi Ikehata, Hang Yang, and Yasutaka Furukawa. Struc- tured indoor modeling. In IEEE International Conference on Computer Vision (ICCV), 2015

work page 2015
[1]

3d semantic parsing of large-scale indoor spaces

Iro Armeni, Ozan Sener, Amir R Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, and Silvio Savarese. 3d semantic parsing of large-scale indoor spaces. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

work page 2016
[2]

Piecewise planar and compact ﬂoorplan reconstruction from images

Ricardo Cabral and Yasutaka Furukawa. Piecewise planar and compact ﬂoorplan reconstruction from images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014

work page 2014
[3]

Realtime multi-person 2d pose estimation using part afﬁnity ﬁelds

Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh. Realtime multi-person 2d pose estimation using part afﬁnity ﬁelds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

work page 2017
[4]

Chang, Angela Dai, Thomas A

Angel X. Chang, Angela Dai, Thomas A. Funkhouser, Ma- ciej Halber, Matthias Nießner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3d: Learning from rgb-d data in indoor environments. In 2017 International Conference on 3D Vision (3DV), 2017

work page 2017
[5]

Layout estimation of highly cluttered in- door scenes using geometric and semantic cues

Yu-Wei Chao, Wongun Choi, Caroline Pantofaru, and Sil- vio Savarese. Layout estimation of highly cluttered in- door scenes using geometric and semantic cues. In In- ternational Conference on Image Analysis and Processing (ICIAP), 2013

work page 2013
[6]

Scannet: Richly-annotated 3d reconstructions of indoor scenes

Angela Dai, Angel X Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In IEEE Conference on Computer Vision and Pattern Recog- nition (CVPR), 2017

work page 2017

Show all 30 references

[7]

https://github.com/multimodallearning/ pytorch-mask-rcnn

pytorch-mask-rcnn. https://github.com/multimodallearning/ pytorch-mask-rcnn
[8]

A dynamic programming approach to reconstructing build- ing interiors

Alex Flint, Christopher Mei, David Murray, and Ian Reid. A dynamic programming approach to reconstructing build- ing interiors. In European Conference on Computer Vision (ECCV), 2010

2010
[9]

Manhattan scene understanding using monocular, stereo, and 3d features

Alex Flint, David Murray, and Ian Reid. Manhattan scene understanding using monocular, stereo, and 3d features. In IEEE International Conference on Computer Vision (ICCV), 2011

2011
[10]

Deep ordinal regression net- work for monocular depth estimation

Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Bat- manghelich, and Dacheng Tao. Deep ordinal regression net- work for monocular depth estimation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

2018
[11]

Seitz, and Richard Szeliski

Yasutaka Furukawa, Brian Curless, Steven M. Seitz, and Richard Szeliski. Manhattan-world stereo. In IEEE Com- puter Society Conference on Computer Vision and Pattern Recognition (CVPR), 2009

2009
[12]

Girshick

Kaiming He, Georgia Gkioxari, Piotr Doll ´ar, and Ross B. Girshick. Mask r-cnn. In IEEE International Conference on Computer Vision (ICCV), 2017

2017
[13]

Varsha Hedau, Derek Hoiem, and David A. Forsyth. Recov- ering the spatial layout of cluttered rooms. In IEEE Interna- tional Conference on Computer Vision (ICCV), 2009

2009
[14]

Learning structured inference neural networks with label relations

Hexiang Hu, Guang-Tong Zhou, Zhiwei Deng, Zicheng Liao, and Greg Mori. Learning structured inference neural networks with label relations. In IEEE Conference on Com- puter Vision and Pattern Recognition (CVPR), 2016

2016
[15]

Scenenn: A scene meshes dataset with annotations

Binh-Son Hua, Quang-Hieu Pham, Duc Thanh Nguyen, Minh-Khoi Tran, Lap-Fai Yu, and Sai-Kit Yeung. Scenenn: A scene meshes dataset with annotations. In 2016 Fourth International Conference on 3D Vision (3DV), 2016

2016
[16]

Learning to parse wireframes in images of man-made environments

Kun Huang, Yifan Wang, Zihan Zhou, Tianjiao Ding, Shenghua Gao, and Yi Ma. Learning to parse wireframes in images of man-made environments. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

2018
[18]

Roomnet: End-to-end room layout estimation

Chen-Yu Lee, Vijay Badrinarayanan, Tomasz Malisiewicz, and Andrew Rabinovich. Roomnet: End-to-end room layout estimation. In IEEE International Conference on Computer Vision (ICCV), 2017

2017
[19]

Manhattan- world urban reconstruction from point clouds

Minglei Li, Peter Wonka, and Liangliang Nan. Manhattan- world urban reconstruction from point clouds. In European Conference on Computer Vision (ECCV), 2016

2016
[21]

Raster-to-vector: Revisiting ﬂoorplan transforma- tion

Chen Liu, Jiajun Wu, Pushmeet Kohli, and Yasutaka Fu- rukawa. Raster-to-vector: Revisiting ﬂoorplan transforma- tion. In IEEE International Conference on Computer Vision (ICCV), 2017

2017
[22]

Brostow, and Niloy Jyoti Mitra

Aron Monszpart, Nicolas Mellado, Gabriel J. Brostow, and Niloy Jyoti Mitra. Rapter: rebuilding man-made scenes with regular arrangements of planes. ACM Trans. Graph., 34:103:1–103:12, 2015

2015
[23]

Polyﬁt: Polygonal sur- face reconstruction from point clouds

Liangliang Nan and Peter Wonka. Polyﬁt: Polygonal sur- face reconstruction from point clouds. In IEEE International Conference on Computer Vision (ICCV), 2017

2017
[24]

Sch¨onberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and An- dreas Geiger

Thomas Sch ¨ops, Johannes L. Sch¨onberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and An- dreas Geiger. A multi-view stereo benchmark with high- resolution images and multi-camera videos. InIEEE Confer- ence on Computer Vision and Pattern Recognit...

2017
[25]

Efﬁcient structured prediction for 3d indoor scene understanding

Alexander G Schwing, Tamir Hazan, Marc Pollefeys, and Raquel Urtasun. Efﬁcient structured prediction for 3d indoor scene understanding. In IEEE Conference on Computer Vi- sion and Pattern Recognition (CVPR). IEEE, 2012

2012
[26]

Chang, Manolis Savva, and Thomas A

Shuran Song, Fisher Yu, Andy Zeng, Angel X. Chang, Manolis Savva, and Thomas A. Funkhouser. Semantic scene completion from a single depth image. In2017 IEEE Confer- ence on Computer Vision and Pattern Recognition (CVPR) , 2016

2016
[27]

Reconstructing the world’s museums.International Journal of Computer Vision, 110(3):243–258, 2014

Jianxiong Xiao and Yasutaka Furukawa. Reconstructing the world’s museums.International Journal of Computer Vision, 110(3):243–258, 2014

2014
[28]

Scene graph generation by iterative message passing

Danfei Xu, Yuke Zhu, Christopher B Choy, and Li Fei-Fei. Scene graph generation by iterative message passing. InPro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

2017
[29]

Funkhouser

Fisher Yu, Vladlen Koltun, and Thomas A. Funkhouser. Di- lated residual networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

2017
[30]

Bighand2

Shanxin Yuan, Qi Ye, Bjorn Stenger, Siddhant Jain, and Tae- Kyun Kim. Bighand2. 2m benchmark: Hand pose dataset and state of the art analysis. In IEEE Conference on Com- puter Vision and Pattern Recognition (CVPR), 2017

2017

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [20]

Floornet: A uniﬁed framework for ﬂoorplan reconstruction from 3d scans

Chen Liu, Jiaye Wu, and Yasutaka Furukawa. Floornet: A uniﬁed framework for ﬂoorplan reconstruction from 3d scans. In European Conference on Computer Vision (ECCV), 2018

work page 2018

[2] [17]

Struc- tured indoor modeling

Satoshi Ikehata, Hang Yang, and Yasutaka Furukawa. Struc- tured indoor modeling. In IEEE International Conference on Computer Vision (ICCV), 2015

work page 2015

[3] [1]

3d semantic parsing of large-scale indoor spaces

Iro Armeni, Ozan Sener, Amir R Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, and Silvio Savarese. 3d semantic parsing of large-scale indoor spaces. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

work page 2016

[4] [2]

Piecewise planar and compact ﬂoorplan reconstruction from images

Ricardo Cabral and Yasutaka Furukawa. Piecewise planar and compact ﬂoorplan reconstruction from images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014

work page 2014

[5] [3]

Realtime multi-person 2d pose estimation using part afﬁnity ﬁelds

Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh. Realtime multi-person 2d pose estimation using part afﬁnity ﬁelds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

work page 2017

[6] [4]

Chang, Angela Dai, Thomas A

Angel X. Chang, Angela Dai, Thomas A. Funkhouser, Ma- ciej Halber, Matthias Nießner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3d: Learning from rgb-d data in indoor environments. In 2017 International Conference on 3D Vision (3DV), 2017

work page 2017

[7] [5]

Layout estimation of highly cluttered in- door scenes using geometric and semantic cues

Yu-Wei Chao, Wongun Choi, Caroline Pantofaru, and Sil- vio Savarese. Layout estimation of highly cluttered in- door scenes using geometric and semantic cues. In In- ternational Conference on Image Analysis and Processing (ICIAP), 2013

work page 2013

[8] [6]

Scannet: Richly-annotated 3d reconstructions of indoor scenes

Angela Dai, Angel X Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In IEEE Conference on Computer Vision and Pattern Recog- nition (CVPR), 2017

work page 2017