CAGE: Continuity-Aware edGE Network Unlocks Robust Floorplan Reconstruction
Authors: Yiyi Liu
Chunyang Liu
Bohan Wang
Weiqin Jiao
Bojian Wu
Lubin Fan
Yuwei Chen
Fashuai Li
Biao Xiong
Venue: NeurIPS 2025 poster ()
Keywords: floorplan reconstruction
continuity-aware edge
representation learning
point clouds
density map
Primary area: applications

Abstract: We present CAGE (Continuity-Aware edGE) network, a robust framework for reconstructing vector floorplans directly from point-cloud density maps. Traditional corner-based polygon representations are highly sensitive to noise and incomplete observations, often resulting in fragmented or implausible layouts. Recent line grouping methods leverage structural cues to improve robustness but still struggle to recover fine geometric details. To address these limitations, we propose a native edge-centric formulation, modeling each wall segment as a directed, geometrically continuous edge. This representation enables inference of coherent floorplan structures, ensuring watertight, topologically valid room boundaries while improving robustness and reducing artifacts. Towards this design, we develop a dual-query transformer decoder that integrates perturbed and latent queries within a denoising framework, which not only stabilizes optimization but also accelerates convergence. Extensive experiments on Structured3D and SceneCAD show that CAGE achieves state-of-the-art performance, with F1 scores of 99.1% (rooms), 91.7% (corners), and 89.3% (angles). The method also demonstrates strong cross-dataset generalization, underscoring the efficacy of our architectural innovations. Code and pretrained models are available on our project page: https://github.com/ee-Liu/CAGE.git.

CAGE: Continuity-Aware edGE Network Unlocks Robust Floorplan Reconstruction
Yiyi Liu' Chunyang Liu' Bohan Wang' Weiqin Jiao²
Bojian Wu Lubin Fan Yuwei Chen' Fashuai Li Biao Xiong
Wuhan University of Technology Uniivesitty of Twente *Independent Researcher d Hangzhou Institute for Advanced Study, University of Chinese Academy Sciences The Advanced Laser Technology Laboratory of Anhui Province *Corresponding Authors: lifashuai@gman com, b xiiongwhuut edu. cn
Abstract
We present CAGE (Continuity-Aware edGE) network, a robust framework for reconstructing vector floorplans directly from point-cloud density maps. Tra- ditional comer-based polygon representations are highly sensitive to noise and incomplete observations, often resulting in fragmented or implausible layouts. Recent line grouping methods leverage structural cues improve robustness but still struggle to recover fine geometric details. To address these limitations, propose native edge-centric formulation, modeling each wall segment as directed, geometrically continuous edge. This representation enables inference of coherent floorplan structures, ensuring watertight, topologically valid room boundaries while improving robustness and reducing artifacts. Towards this de- sign, we develop dual-query transformer decoder that integrates perturbed and latent queries within a denoising framework, which not only stabilizes optimiza- tion but also accelerates convergence. Extensive experiments Structured3D and SceneCAD show that CAGE achieves state-of-the-art performance, with Fl scores of 99.1% % (rooms), 91.7% (corners), and 89.3% (angles). The method also demonstrates strong cross-dataset generalization, underscoring the efficacy of our architectural innovations. Code and pretrained models are available on our project page:
com/ Liu/ Bauu/AAE..E
Introduction
Reconstructing indoor scenes into compact, editable vector floorplans is a longstanding goal in computer vision and robotics 2 A vector floorplan a structured 2D representation of interior geometry, composed of lines polygons that delineate walls, room boundaries, and architectural elements. Unlike raster maps, vector floorplans are resolution-indest iooluuiion-ideeeedeent support precise geometric reasoning, and integrate seamlessly with CAD tools and building information models (BIM). Their joint encoding of spatial topology and geometry makes them ideal for downstream applications such as building lifecycle management, AR/VR simulation, and autonomous navigation 3 5 6).
Modern floorplan reconstruction uses diverse inputs including RGB images 7 panoramic views 8
CAD drawings [9]. and 3D point clouds [10]. Among these, projecting point clouds to 2D density maps provides an optimal balance of geometric accuracy and computational efficiency [T] [2]. making it popular in learning- based approaches. However, real-world scans often suffer from severe occlusions, clutter, and incompleteness that obscure critical structural elements. Additionally, the discretization process in density map creation blurs corner features and wall segments. These challenges significantly complicate accurate vector floorplan recovery. The resulting incomplete or noisy representations demand robust algorithms capable of handling such imperfect data.
Notably, critical factor affecting reconstruction quality lies in the choice of floorplan representation. Recent work has explored a range of methods. For example, HEAT [13 detects discrete corners and
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(A) Ireue Point Cloul
(b) Denityy Map
(clhrddicted
Sequeross
(0) Rusonsmmeced
FloutPee
(6) Ground Trulh
Figure I: Floorplan reconstruction using our CAGE network. Given an input point cloud (a), we project into 2D density map (b), predict two-level edge sequences (c), and reconstruct vector floorplan by intersecting the predicted edges (d). Note that, our edge-based formulation enables the recovery of regular, topologically valid polygons even in severely occluded regions (highlighted with red circles). See Figure 6 for comparisons with comer-based and other SOTA methods.
connects them into edges and polygons, yet it often suffers from gaps caused by missed detections. RoomFormer [12] models floorplans as sequences of corners using two-level query transformer, yet single missing corner can distort the entire layout. As a result, corner-based methods are highly sensitive to noise. SLIBO-Net [[4] introduces slicing-box representation but relies heavily on the Manhattan-world assumption. FRI-Net [15] reconstructs rooms by compositional line grouping, its implicit neural representations often lead to over-smoothing of fine structures. It is clear that current forms still struggle to capture global structure while keeping local precision.
To address these challenges, we propose CAGE (Continuity- edGE) network, a robust frame- work for reconstructing vector floorplans from density maps. Our CAGE adopts an edge-centric formulation, modeling each wall as a directed, geometrically continuous edge. This design improves robustness incomplete or noisy data by removing reliance on precise corner localization, while enabling global reasoning. To complement this representation, we introduce a dual- -ueryy transformer decoder that integrates perturbed and latent edge queries within a denoising framework, enhanc- ing training stability and accelerating convergence. As decoding progresses, edge predictions are iteratively refined, yielding clean and watertight polygons. Evaluations on Structured3I) [16] and SceneCAD [17] demonstrate that CAGE achieves state-of-the-art performance and generalizes well across datasets. Our main contributions are as follows:
Continuity-Aware Edge Representation: We introduce an edge-based polygon formulation that enhances robustness to incomplete and noisy data, while preserving directional and structural consistency.
Dual-Quert Transformer Decoder:
Our approach leverages perturbed and latent queries within denoising framework to stabilize training, refine predictions, and accelerate conver- gence.
Strong Performance and Generalization: We achieve state-of-the-e performance on multiple benchmarks and generalize effectively across datasets, validating the effectiveness of our continuity- aware edge-based formulation.
Related Work
Approaches floorplan reconstruction can be broadly categorized into three groups: traditional methods based classical geometric machine learning techniques, stage-wise learning- based pipelines that combine hand-crafted and learned components, and fully end-to-end learning methods.
2.1 Traditional Methods
Early work in floor- and room- room-layout recovery relied on low-level vision cues or geometric heuris- tics. Some methods extract planes from point clouds and optimize line placements to assemble
floorplans [8 19 while others infer structure from panoramic images 20 g CAD draw- ings 99
Techniques such as RANSAC-based plane fitting and piecewise merging have been used generate compact surface meshes that are later flattened into floorplans (23.. Graph- formulations often cast the reconstruction as shortest-patt or global optimization problems, producing watertight layouts but requiring hand-designed potentials [24] 25 More recent pipelines focus on han- dling cluttered LiDAR scans via global optimization over curved surfaces (26 or decomposing scenes into walls and objects for lightweight BIM generation (27]. From single panorama, Pano2CAD
21
22
estimates Manhattan room geometry and object pose [28 while successors like PanoFormer 29

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support curved wall recovery via tessellated spherical surfaces. Recent work also explores LoD4 building modeling by fusing interior and exterior cues from SfM images [30]. Traditional methods require small amounts of training data while heavily relying on the design of handcrafted features.
2.2 Stage-wise Learning-based Methods
Several methods adopt hybrid pipeline where learning components are combined with post- processing optimization. FloorNet [31] detects corners in a top-view raster and assembles them via integer programming, while HEAT [13] replaces the solver with transformer-basee
corner-pane classifier. Floor-SP [11] and MonteFloor 32] segment rooms using Mask-RCNN [33], refine initial contours through shortest path or Monte Carlo Tree Search [34 [5] and encourage geometric con- sistency by sharing corners and walls. PolyGraph [36] generates wall points using a cross-guided neural network, forms initial triangles, and applies post-processing to refine polygonal layouts. FloorUSG [19] integrates 2D plane instances with 3D geometry to lift RGB features into structured floorplans. ArrangementNet [10] detects walls and partitions space using wall lines, then leverages an extended GCN to model collinearity and coplanarity among surface patches, improving segmen- tation in complex scenes. SLIBO-NG [14] introduces slicing box representation with geometric regularization and post-proce post-ssocsssing for capturing fine local details. While these stage-wiss wise systems achieve strong performance, they are often sensitive to missing primitives and rely extensive post-optimization.
2.3 End-to-End Learning-basee based Methods
Recent advances favor end-to-end pipelines that directly predict vector floorplan using transformer- based decoders. RoomFormer [12] introduces transformer architecture with hierarchical queries to jointly predict multiple corner sequences. Although effective, it suffers from unordered predictions due random query initialization and incomplete polygons caused by missed corners. PolyRoom (37) enhances this framework by segmenting rooms with Mask-RCNN (33 and initializing RoomFormer- style queries from detected contours. PolyDiffuse (38) formulates floorplan generation conditional task and introduces guided set diffusion model for room representation optimization. These corner- based methods treat floorplan reconstruction as discrete corner regression, often neglecting global shape coherence. FRI-Net [5 addresses this by learning room-wise latent codes and decoding them into lines, which are grouped into polygons using a BSP-Net- -SP--Ne-insppred 39 grouping strategy. Recent methods have progressed from corner- based to tine-grouping approaches, highlighting the importance of structural information. However, they still fail balance robustness with geometric precision. We address this with ddge-centric representation that fundamentally reformulates the problem, enabling accurate, topologically valid reconstructions an end-to-end trainable framework.
(a) Corner-based Representation
(b) Edge-based Representation
(c) Two-level
Edge Tokens
(d) Density Map
(e) Floorplan
Figure 2: Edge-based Floorplan Representation. (a) Corner-based: polygons defined by sequential vertices; (b) Edge-based: walls represented as directed edges with geometric continuity: (c) Tokeniza- tion: polygons represented as sequences of edge tokens at two levels: (d) Density map generated from point cloud: (e) Ground-truth floorplan. While edge and corner representations are mathematically dual, the edge-based formulation offers greater robustness to noise and occlusion (highlighted in red).
Edge-based Floorplan Representation
We propose an explicit edge-based representation for modeling floorplan, addressing the key limita- tions of prior methods, see Figure (b)).. We represent each room polygon as ordered sequence of directed edges, where each edge defined by a pair of endpoints normalized space, as illustrated in 2 Let floorplan contain most M rooms, and each room contain most N

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edges. We represent the m-th room as:
Nm Rm { m=1 where en = (P Pm22
(I)
Each edge en R4 connects points pm Pin E (0, 1]2 The edge order encodes the polygon's
directional traversal. To support batched training and variable-length polygons, we augment each edge with binary validity label:
* = (P Pm2z: s.)), 2 E {0, 1}
(2)
Here, c 0 denotes invalid (padded) edge, and each in serves as edge token, illustrated in Figure 2 c). edges in polygon are invalid, the entire room treated as padding. While edge and corner representations are dual, edges empirically more stable in sparse noisy conditions, since they encode directionality explicitly, promoting angular regularity and structural coherence. Unlike corner-based methods that require precise vertex localization, edge-based design allows endpoints to lie along wall segments. The flexibility simplifies spatial queries and enables the model to infer global structure even from partial observation. We aggregate edge tokens into fixed-length set:
Tm {tm V.V
mm///11 where max Nm S N
(3)
and define the full floorplan as:
F= FT [TmJm=
This edge-based representation models room layouts fixed-length sequences of spatially grounded edge tokens, which align well with transformer architectures, facilitate spatial attention over relevant image regions, and support stable training through bipartite matching.
9111
III
(a) Input edge sequence
(b) Four intersection types
(c) Ousput polygon
Figure 3: Illustration of dige-to--plygon conversion. (a) Input edge sequence; (b) Four types of edge intersections based on geometric proximity; (c) Final reconstructed polygon.
To convert edge sequences into closed polygons, we resolve edge intersections into vertices. As illustrated in Figure 3 we identify four types of pairwise edge intersections. Types I-Ill involve intersection points that lie on or near the edges and are retained as valid polygon vertices. In Type IV, where the intersection is far from any endpoints, preserve continuity. As all edges are ordered and directed, this conversion remains deterministic and robust even under noisy predictions.
instead connect the nearest edge endpoints to
4 Method
4.1 Overview
Figure illustrates the overall architecture of our proposed CAGE framework for floorplan recon- struction from 3D point clouds. The input point cloud is first projected onto the XY-plane to generate a 2D density map, which is processed by a convolutional image backbone to extract multi-scale visual features. The output feature maps are flattened and augmented with positional encodings to form unified token sequence, which is passed to transformer encoder. We employ multi-scale deformable attention [40] in the encoder to efficiently aggregate both local and global spatial context across all feature levels.
The encoded features are processed by our novel transformer decoder composed of stacked attention layers. The decoder operates on set of learnable edge queries, which are refined across layers through
mechanisms. Each query predicts directed edge denoted by two endpoints and is assigned a binary label indicating whether it constitutes valid segment, as described in Sec. 3 To improve the robustness and training stability, we incorporate dual-query design that includes latent queries for final predictions and perturbed queries for denoising supervision. The following subsections will illustrate our novel dual-query decoding mechanism and loss formulation.

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Perturbed
Denioised
Expertel
Quartes
Polygons
Def. Cross Attn.
ckbboo
Image
Encaadar
Transformer
Self Self-Attn. Attn.
FFN
Crcos Aetes
Sell Ada
Querles
Polygons
Doppe
Drishiins
(a) Density Map and Encoder
(b]Transformer Decoder
(c) Polygon Matching
(d) ith Layer in the Decoder
Figure 4: Architecture of the CAGE network. (a) The input point cloud is projected into density map and processed by an image backbone and transformer encoder. (b) The transformer decoder receives two types of queries (perturbed and latent) and predicts edges defined by two endpoints, which may not correspond to polygon vertices. (c) feed-forward network assigns class labels, with polygon matching for supervision. (d) Detailed architecture of decoder, showing the progressive refinement of edge queries with the incorporation of the novel designs of perturbation and denoising.
iterationn
iteration?
iteration
iteration
iteration
iteration
Figure 5: Polygon evolution during decoding. Top: edge query predictions refined over six decoder layers. Bottom: corresponding polygon reconstruction becomes more accurate and complete.
4.2 Iterative Polygon Refinement by Dual Query
To enable precise and stable polygon prediction, we design dual-query transformer decoder that refines edge representations over multiple decoding layers. As shown in Figure dd), the decoder jointly operates on two sets of edge queries: perturbed queries used for denoising- based supervision, and latent queries responsible for final polygon prediction. The denoising strategy encourages the model recover clean edge structures from deliberately corrupted inputs, improving its robustness to noise and accelerating convergence. This dual-query design strengthens learning stability, enhances generalization, and maintains full end-to-end differentiabilityy
As described in Sec. 3. we represent each room as a sequence of edges and flatten all edges into query tensor as maximum number of edges per polygon. Each query predicts an edge defined by two endpoints. The decoder layers refine these queries iteratively, illustrated in Figure [ where edge geometry and corresponding polygons become increasingly accurate across decoding steps.
MxXx2 where M is the maximum number of polygons and N is the
Each layer receives positional queries derived from polygon coordinates via sinusoidal encoding, along with content queries attending to multi-scale encoder features. Self-attention allows intra- polygon edge refinement and inter- nnter-polygon context exchange. Cross- modules associate each edge query with spatially grounded encoder features, guided by its predicted endpoint coordinates. This supports effective reasoning over both local structure and global context. A binary classifier identifies edge validity, allowing the model to handle variable polygon structures.
Following Deformable DETR 40), we refine edge coordinates through iterative offset prediction. Each decoder layer outputs coordinate offsets that adjust the endpoints of every edge query, improving geometric precision across layers. Latent queries are randomly initialized and optimized for prediction, while perturbed queries are derived by adding controlled noise to ground-truti edge coordinates. These two query types are processed jointly in the decoder.
5

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To support denoising supervision, we adopt training objective inspired by DN-DETR [1]. The decoder receives perturbed queries generated by adding controlled noise to ground truth edges and optionally flipping their class labels. Let the perturbed query set be q {go..... 9MM N- ). and the latent query set be Q = {Qu-- .... QWN-1), The decoder operates on both sets under an attention mask A that prevents leakage between perturbed instances:
0 D(q. Q.q.Q.FIA)
where F denotes encoder features.
Each perturbed edge query is generated by applying controlled noise to a ground-truth edge during training. Specifically, for each edge, its endpoints are randomly displaced simulate input uncertainty. The displacements are constrained relative to the spatial extent of the density map: the horizontal shift Axr and vertical shift Ay bounded by Aa| Aw/2 and |Ayl Xh/2, where w and h denote the width and height of the input density image, respectively. If perturbed endpoint falls outside the image boundary, it clamped to remain within the valid range. The scalar À is hyperparameter that determines the magnitude of perturbation. Additionally, to introduce label noise, the binary class label of each edge is randomly flipped with probability ". This denoising strategy encourages the decoder to recover clean edge structures from noisy inputs. During inference, only the latent queries are used to generate final polygons.
4.3 Loss Functions
To train the CAGE network in end-to-end manner, formulate loss that supervises both the structure and geometry of predicted polygons. The decoder outputs a fixed number M of polygons, each consisting of up to directed edges. Since the number and order of edges vary across ground-truth annotations, we adopt two-level matching strategy that aligns predictions with targets.
Polygon Matching. Let the predicted edge set be Š { = (é). ..., e JM m= where each edge en = (ch Pm Pm2) includes binary confidence score and two endpoint coordinates. Ground- truth polygons are denoted as S = Em m= each padded to length and further extended with mock polygons for matching. We compute bipartite matching between predicted and ground-truth polygons using the Hungarian algorithm, minimizing the following assignment cost:
M
δ = arg min
Σ D(Em, Bo(m))
(6)
where the matching cost includes both classification error and edge regression:
N
N
2
D(Ems Em(m)) = 1l (m)
Adts
2
2
(m(m)
+
Pink
Pa Po((a)))
m=1
To account for cyclic nature of polygons, we evaluate distances over all valid rotations of ground-truth sequence and choose the minimal one.
Loss Components. After matching, we supervise the predicted polygons using three terms: classi- fication, edge regression, and rasterization. For latent queries, losses are applied only to matched predictions. For perturbed queries, losses are directly applied without the matching step, as their correspondence to ground-trut truth edges is already known.
The classification loss uses binary cross-entropy:
4
Σ
log (m)) (1-c,) log(1 2d(m)))
(8)
The edge regression loss is the ε distance between predicted and ground-truth endpoints:
N...
1
edge
N
{ms Ma3
Pink
(
(9)
The rasterization loss measures the overlap between polygon masks using the Dice loss
42
4m = (mS Dice (R(Pm), R(Pa(m))
(10)

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where R(-) denotes a differentiable polygon rasterizer 43
For perturbed queries, apply denoising losses without matching. These include binary cross- entropy for classification and & regression for edge localization, denoted as Lets DN and Lodge DN- The total training loss is the weighted sum of all components:
M Σ
C
(Ness +
+ Aea Cos + Det_
C DN + Aodge DN DN DN)
(11)
Experiments
5.1 Experiments Setting
Dataset and Evaluation Metries. We evaluate our method on two large-scale indoor datasets, including Structured3D [16] and SceneCAD [17] Structured3D is photo-realistic synthetic dataset comprising 3,500 houses with diverse Manhattan and Mannataan layouts. SceneCAD provides 3D room layout annotations for real-world RGB-D scans from ScanNet [4], with each sample containing a single room. Following previous work [33 121, we split Structured3D into 3,000 training, 250 validation, and 250 test samples. For SceneCAD,
use the provided splits of 828 we process registered multi-view RGB-D panoramas into point clouds, and project them vertically into 256 x 256 pixel density images. Each pixel value is normalized to [0, by dividing the number of projected points by the maximum count per image. In line with prior work 12 (6),, we evaluate performance using Precision, Recall, and Fl scores at the Room, Corner, and Angle levels.
training and 127 validation samples. Following 32 [3,
Baselines, We compare our method with eight state-of-the-art approaches: Floor-SP SP [TI, HEAT [13. RoomFormer [1) SLIBO-Net (14. FRI-Net [I5], PolyRoom [37], PolyGraph 336 and PolyDif- fuse 38 SP 11 uses Mask-RCNN for room segmentation followed by classical optimization for vectorization. HEAT 13 detects corners and infers connectivity with a neural network. Room- Former 12 employs transformer to jointly predict multiple corner sequences. SLIBO-Net [14] generates slicing boxes and room centroids via transformer decoding, refined through post-processing. FRI-Net [5] encodes room-wise latent features, decodes them into lines, and assembles polygons using a BSP-Nettinsspreed 39 grouping strategy. PolyRoom [37] combines Mask-RCNN- seg-
mentation with KoomFooree--syle reconstruction initialized from detected contours. PolyGraph [36] predicts wall points and triangulations and then refines results by postprocessing. PolyDiffuse formulates reconstruction conditional generation task, using diffusion model to refine floorplans from initial proposals iteratively.
38
Implementation Details. Our model implemented in PyTorch and trained on a single NVIDIA RTX 4090 GPU. We use Swin Transformer V2 [45] the default image backbone, while ResNet- 50 [46] and Swin Transformer V1 [47] are used in ablation studies. The transformer architecture follows standard 6-layer encoder-decoder configuration. We apply denoising training with dual- query supervision and optimize using Adam (48). Further model configurations, training schedules, and hyperparameters are provided in supplementary material
5.2 Comparisons to State-of-the-Art Methods
Table 1:
Quantitative comparison on the Structured3D 16 test set. The rumning time is averaged over the whole test PD denotes PolyDiffuse (88 post-optimization. *; Params are calculated from the officially released models.
Method
Venue
Backboae
Pa)ams(M]*
((s)
Room
Corner
Angle
Prec:
Rec.
Prec:
Rec.
Fl
Prec.
Rec.
Fl
HEAT [3]
CVPR22
ResNet-50
48.9
0.11
96.9
94.0
95.4
81.7
83.2
82.5
77.6
79.0
78.3
RoomFormer
12
CVVR23
ResNet-50
40.6
0.01
97.9
96.7
97.3
89.2
85.3
87.2
83.0
79.6
81.3
SLIBO-Net
[4
NeudPS23
ResNet-50
99.1
97.8
98.4
82.1
85.4
87.8
81.2
84.4
PolyRoom
33
ECCV24
ResNet-50
42.2
0.02
98.9
97.7
98.3
94.6
86.
90.2
89.3
$1.4
85.2
FRI-Net
BCCV24
ResNet-50
98.7
87.8
PolyGraph
X0
TVCG25
ResNet-50
0.04
95.7
97.9
96.7
92.4
82.2
88.3
89.2
79.4
85.4
Our
SwinV2-1.
211.3
0.01
99.6
98.7
99.1
95.0
88.6
91.7
92.5
86.4
89.3
RoemForeerPPD 3g
NeudPS23
RosNet-50
98.7
98.1
98.4
92.8
89.3
91.0
90.8
87.4
89.1
PRI-Net-PD
[5
BCCV24
ResNei-50
99.6
98.6
99,1
94.2
88.2
91.1
91.9
86.7
89.2
Ours+PD
SwinV2-L
99.7
98.7
99.1
95.1
89.9
91.7
91.6
87.5
89.2

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Density Map
GT
НEАТ
PolyGraph
RoomFormer
FRI-Net
PolyRoom
Our
2622
8336
66%S
34944
Figure 6: Qualitative comparisons on Structured3D [6]. Scene IDs are shown on the left. Red circles
highlight challenging regions.
Quantitative Evaluation. Quantitative comparisons against state-of-the-art floorplan reconstruction methods on the Structured3D [16] dataset are presented Table We compare our method with HEAT [13. SLIBO-Net [14), PolyRoom (37). FRI-Net [15], and PolyGraph [36]. For completeness, we also include post-optimization results using PolyDiffuse [38]. Without any post-processing, our method achieves the best performance across all geometric levels, reaching 99. % Room F1, 91.7% Corner F1, and 89. 99.3% Angle F1. Although FRI-Net attains comparable performance
Room Recall and Room FI, CAGE surpasses it by notable margin of +3.9 points in Corner FI and +2.4 points in Angle F1, demonstrating superior accuracy in fine ane-ggrined corner localization and angular estimation.
After applying PolyDiffuse for post-optimization, our method further consolidates its leading position across all three evaluation levels, outperforming both RoomFormer and FRI-Net. Interestingly, the post-optimization with PolyDiffuse brings only marginal improvements to our model, indicating that CAGE already captures the scene structure effectively without reliance heavy post-processing. In contrast, PolyDiffuse, as a diffusion- model, requires substantial additional training time. Furthermore, despite not explicitly optimizing for corner predictions. our method maintains competi- tive performance in Corner Recall. Regarding efficiency, CAGE achieves the fastest inference speed, along with RoomFormer (both 0.01 second per image), benefiting from its lightweight architecture without the need for additional refinement stages. Overall, these results demonstrate that CAGE strikes an excellent balance between reconstruction accuracy and computational efficiency.
Density Map
RoomFormer
PotyRoom
FRI-Net
Our
Denotly Map
RoomFormer PolyRoom
FRI
Our
Figure 7:
Qualitative
evaluations on
Figure 8: Cross-dataset evaluation (train-
SceneCAD 17
ing on Structured3D 16 and testing on SceneCAD [7]).

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On the SceneCAD 17 dataset, we further evaluate our method against Floor-SP [TI HEAT [3] RoomFormer L2. FRI-Net [T3]. and PolyRoom [37]. Given that SceneCAD primarily consists of single-room layouts, we assess reconstruction quality using IoU for room shape. As shown in Table CAGE consistently achieves either the best or second beoodd performance across nearly all evaluation metrics. Specifically, our method attains the highest scores in Room IoU, Corner Recall, Angle Recall, and Angle FI, while securing second-best results in other metrics. In comparison, PolyRoom achieves the highest Corner Precision and Corner FI but performs slightly lower on remaining metrics. Overall, CAGE demonstrates strong and balanced performance across all aspects of room layout reconstruction on the SceneCAD dataset.
Qualitative Evaluation. Visual comparisons on Structured3D [16] are shown in Figure 6 with red circles highlighting challenging regions for reconstruction. Most state-of-the-ar methods fail to recover severely occluded or blocked areas, while our method successfully infers structures using weak wall cues (e.g., scene 3292). HEAT [3] and PolyGraph [36] reconstruct wall centerlines, whereas other methods produce double-wal representations, with each polygon denoting room interior boundaries. HEAT follows a two-stage detects--eeectingg corners and inferring connectivity-which often leads to missing corners and incorrect edge associations (e.g., scene 3493), resulting in open polygons. PolyRoom 37 generates simple, closed polygons but struggles occluded regions 3292, 3356). RoomFormer [12] predicts polygonal layouts via sequences of corners, ensuring closure but often producing irregular shapes due to weak constraints on edge relationships (c.g., 3292, 3493, 3484). While FRI-Net [15] and PolyRoom demonstrate better regularity and partial robustness to occlusion, they still fail under heavy blockage (c.g.. 3292, 3356). In contrast, our method explicitly models global structure through edge-aware representation and denoising srnnee training, leading to more regular room geometries and robust reconstruction in occluded scenarios. Figure further confirms this trend, with our model consistently producing more plausible results compared to corner-based optimization methods.
Cross-Dataset Generalization. In cross-datasel evaluation (training on Structured3D [6 and testing on SceneCAD (17), PolyRoom and CAGE achieve the top results across Room IoU and fine-level corner/angle metrics (Table B}. PolyRoom's segmentation- with SceneCAD's single-room layouts, yielding strong room-level performance, but its multi-stage design struggles with the complex multi-room structures in Structured3D (Table D. In contrast, CAGE's end-to-end trainable, edge-based modeling consistently maintains high accuracy (85.6% Room IoU, 87.5% Corner Recall, 70.6% Angle Recall, 61.6% Angle F1) without post-pre post-processing. These results validate that combining edge-basee based representations with global shape modeling and denoising strategies enables CAGE to achieve robust and balanced floorplan reconstruction under domain shifts and varying scene complexities.
pipeline aligns well
Table 2:
Quantitative comparison on the SceneCAD validation set 11 Results for prior methods are reported from
Table 3: Cross-data generalization. Models are trained on Structured3D train set but evaluated on SceneCAD val set.
38
Method
Room I0U
Comeer
Angle
Room
Corner
Angle
Pree
Roc.
Prec.
Rec.
Rooe-SP
91.6
89.4
85.8
87.6
74.3
71.9
73,1
HEAT LB]
52.5
50.9
51.1
51.0
42.2
42.0
41.6
HEAT [5]
86.9
87.8
79.1
83.2
73.2
67.8
70.6
RoomFormer
[
74.0
56.2
65.0
60.3
41.2
18.1
46.2
Roomfommer
[2
91.7
92.5
85.
88.8
78.0
73.7
75.8
66.4
FRI-Net
[3
92.3
92.8
85.9
89.2
78.3
73.6
75.9
PolyRoom
37]
85.2
77.8
79.9
78.9
59.4
6L.7
60.6
Poly
92.8
86.1
91.2
81.7
74.5
78.0
CAGE (Ous)
85.6
68.2
87.5
76.7
51.6
70.6
61.6
CAG))Ours)
77.3
5.3 Ablation Study
We evaluate the impact of three core components: the edge- baged polygon representation. the denoising (DN) training strategy, and the image backbone. As shown in Table 4 both the Edge and DN modules yield consistent improvements individually, and their combination results in further performance gains. By contrast, incorporating SwinnTransfommr-VV V2 the backbone provides only marginal benefits beyond the combined Edge and DN configuration. Further analysis and visual examples provided in Supplementary Material B.5
Polygon Representation via Edge. We first examine the effect of the proposed Edge module, as shown in the third row of Table 4 In this variant, the original corner-based representation in RoomFormer is replaced with edge-based formulation, while keeping all other settings unchanged.

---

Table Ablation study on Structured3D. Edge and DN modules significantly improve performance. Swin'T backbones provide limited or reduced gains compared to ResNet50. RoomFormer [2 results are reproduced using our training setup for fair comparison.
Method
Edge
DN
Backhone
Room
Corner
Angle
Prec.
Rec.
F1
Prec.
Rec.
F1
Prec.
Rec.
F1
RoomFormer
ResNer50 46
96.9
95.9
96.4
87.3
83.8
85.5
81.2
77.9
79.5
Ours
7
ResNet50 46
98.7
97.6
98.2
91.2
87.0
89.0
85.1
81.3
83.2
Ours
ResNet50 46
98.0
96.2
97.1
90.4
84.4
87.3
85.0
79.5
82.2
Ours
SwinT [
97.0
95.7
96.3
87.3
83.4
85.3
80.5
76.9
78.6
Ours
V
ResNet50 46
99.6
98.7
99.2
93.9
88.8
91.3
89.7
84.9
87.2
Ours
SwinT VI 47
99.4
98.5
98.9
94.5
89.1
91.7
91.1
85.9
88.4
Ours
V
Swin'T v 45
99.6
98.7
99.1
95.0
88.6
91.7
92.5
86.4
89.3
The Edge module consistently improves performance across all evaluation metrics, increasing Room Fl from 96.4% to 98.2%, Corner F1 from 85.5% to 89.0%, and Angle FI from 79.5% 83.2%. These results demonstrate that representing polygons by edges enables the model to more effectively capture structural geometry and enhances its ability to localize wall boundaries.
Training Strategy via Denoising. To further improve learning stability and geometric reasoning, we incorporate a denoising strategy in which ground-truth data with noise are introduced alongside random queries during training. This approach supports convergence in the early stages of prediction and stablize the Hungarian Matching process. As illustrated in the third row of Table 4 the addition of the DN module alone leads to moderate gains. While the DN module is less impactful than the Edge module isolation, its value becomes more evident in combination.
When both Edge and DN modules are integrated (fifth row of Table 4 we observe substantial performance gains across all metrics, with Room F1 rising 99.2%, Corner F1 91.3%, and Angle F1 to 87.2%. Notably, the most significant gains are observed in the Corner and Angle metrics, indicating improved precision local geometric structures. This synergy suggests that the Edge and DN modules complement each other by jointly enhancing both global room layout understanding and fine-grained structural details.
Image Backbone. We evaluate the impact of different image backbones Resseet500 446] SwinTransformer.V VI [47), and SwinTransformer-V V2 [4il- keeping the proposed Edge and DN modules fixed. All configurations utilize four image scales and token dimension of 256. As shown in the last three rows of Table integrating Swin Transformer backbones yields mixed results. While Swwifrransfommr-VV achieves the highest Angle F1 score (89.3%) and slightly improves Comer F1 (91.7%), these gains are relatively modest compared to the improvements brought by the Edge and DN modules. Notably, ResNet-50 combined with both modules attains the highest Room Fl score (99.2%) underscoring its effectiveness in capturing global structural context.
These results suggest that the primary performance improvements stem from the proposed architec- tural enhancements rather than the choice of backbone. Swin Transformer variants offer benefits in modeling local geometric relationships through shifted windowing and gross-boundarr attention, particularly enhancing the Corner and Angle metrics. However, the convolutional features of ResNet- 50 remain highly effective for global layout prediction. Overall, SwinTrransforma balanced trade-off between local and global representation capabilities. Therefore, unless otherwise stated, we adopt the configuration with Edge, DN, and SwinTraanffrmer-v2 the default model for subsequent experiments.
provides
6 Conclusion
We present CAGE, an end-to-end floorplan reconstruction framework that models rooms as sequences of edges using a dual-query transformer architecture. By predicting edges instead of corners, our method captures global geometric structure and directional priors, enabling robust inference of room layouts even from partially observed wall segments through edge-based attention. This significantly improves resilience to occlusion and layout noise. A denoising- based training strategy with perturbed and latent queries facilitates stable and efficient iterative refinement. Extensive experiments on Structured3D and SceneCAD demonstrate state-of-the-art performance. Our method still puzzles over reconstructing scenes with dense occlusions or fine structural elements. Future work includes integrating semantic priors, extending to full architectural elements, and scaling outdoor scenarios.
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Supplementary Materials
A Implementation Details
Model Settings. Our primary image backbone Swin Transformer V2 [45], while ResNet-50 [46] and Swin Transformer V1 [47] are used for comparison in the ablation study. Following Room- Former [2], for ResNet-50, we extract multi-scale feature maps from the last three stages without using feature pyramid network (FPN). For Swin Transformers,
adopt the official large-variant configurations: SwinV1 uses Swin-L. pretrained weights, and Swin V2 uses SwinV2-L pretrained weights, both with base dimension of C 192 and stage depths of [2. 2, 18, 2]. The window sizes differ, with Vw using a window size of 12 and Swin using 16. For both ResNet-50 and Swin convolution with stride applied to the final-stage output. All feature maps are projected to 256 channels before being fed into the transformer. Pretrained weights from 46) 47 45 are used for all backbone layers and are fine-tuned during training. Our transformer architecture comprises 6 encoder layers and decoder layers, each with hidden dimension of 256. We set the number of room feature codes m 20 and the number of edge queries n to 40.
Transformer backbones, the fourth-scale feature map is obtained via 3
Training. We train our model using the Adam optimizer [48] with weight decay of 1 Depending on the dataset size, we train the model on Structured3D) for 650 epochs with an initial learning rate of 2,, and SceneCAl) for 400 epochs with initial learning rate of 5e In both cases, the learning rate is decayed by a factor of 0. during the final 20% of epochs. The loss weights are set A 0.6, Aedge 6, A Apn els 0.6, and ÀDN__dee edge 6. For perturbed queries, the noise scale is set to 0.2 for class and 0.4 for edge. We implement our model in PyTorch and conduct all experiments NVIDIA GeForce RTX 4090 GPU with 24 GB of memory.

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B Additional Results and Visualizations
B.1 Evolution of Edge Queries
To further examine the decoding process, visulize layer-wise edge query refinement illustrated in Figure [ As decoding progresses, the predicted edge structures become increasingly coherent and better aligned with the underlying point density map, even in regions with limited or missing wall evidence. Correspondingly, the reconstructed polygons evolve from coarse outlines to precise, watertight layouts. This illustrates the effectiveness of our progressive decoding strategy in improving both geometric accuracy and topological consistency.
iteration
iteration
iteration
iteration"
iteration
iteration
Our Edge
Our Polygon
3456
RoomFormer
Our Edge
Our Polygon
3292
RoomForme
Figure 9: Additional results for edge query evolution and polygon formation. Each example shows density map from Structure3D [16] (scene ID the left), with edge query predictions over six decoding layers (top row), the corresponding polygon reconstructions (second row), and the polygon evolution of Roomformer [12] (third row).

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B.2
More Reconstruction result on Structured3D
Density Map
GT
HEAT
PolyGraph
RoomFormer
FRI-Net
PolyRoom
Our
3410
5344
3486
3295
3443
3442
Figure 10: Additional results for Qualitative evaluations Structured3D 17

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B.3 Failure cases on Structure3D
Our proposed CAGE model demonstrates strong performance on both the Structure31) and SceneCAD datasets. However, several typical failure cases remain, as illustrated in Fig. 11 In some cases, ambiguity in corner completion arises: certain ground truth layouts require polygonal closure at missing corners, while others do not, leading to inconsistencies in prediction (Fig. aa)). Additionally, small architectural elements such as interior rooms columns may be absent from the input density map, resulting in missed detections (Fig. IT)... Severe occlusions caused by missing scan stations can further result in incomplete reconstructions (Fig. [Tfe).. Lastly, we observe variability in prediction quality across different checkpoints within the same training run some
run-some checkpoints yield strong results, while others perform poorly-likely due to sensitivity to parameter initialization or optimization instability (Fig. [T))). Addressing these challenges may involve incorporating more robust data augmentation techniques, exploring reinforcement learning-baat refinement, or leveraging checkpoint ensembles to enhance model stability and consistency.
3255
3335
3312
3454
3313
3260
3325
3252
dejy Deusityy
Jn
(b)
(c)
Figure 11: Failure cases on Structure3D [6] including polygon ambiguity(a), missing structures(b),
incomplete scans(c), and checkpoint instability(d).
B.4 More Reconstruction result on SceneCAD
70401
38201
7700
8802
13900
16400
22201
55200
Point Cloud Density Mag
19
Ou
Figure 12: Additional results for Qualitative evaluations SceneCAD 17

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Table Sensitivity of reconstruction accuracy to threshold values. the threshold we used in our experiments.
Room
Corner
Angle
Eps
Prec.
Rec.
F1
Prec.
Rec.
F1
Prec.
Rec.
Fl
0.2
99.59
98.69
99.14
95.04
88.57
91.70
92.57
86.40
89.39
0.1
99.59
98.69
99.14
94.99
88.55
91.66
92.51
86.37
89.34
0.05
99.59
98.69
99.14
94.96
88.55
91.65
92.46
86.34
89.30
0.01
99.59
98.69
99.14
94.95
88.58
91,66
92.40
86.34
89.27
0.0001
99.59
98.69
99.14
94.94
88.60
91.67
92.38
86.34
89.26
80
Params (M)
FLOPs (G)
Inference time (ms)
GPU Mem (GB x10)
50
30
*
*
X
x
X
0
1.2
14
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
NUMBER TOKENS (K)
Figure 13: Impact of the number of tokens on model complexity and efficiency. As the token count increases, the number of parameters, FLOPs, inference time, and GPU memory usage grow nearly quadratically, reflecting the scalability characteristics of tranformer-basee architectures.
B.5 Extended Evaluation
Sensitivity threshold in edge-to-polygon conversion. The edge-to-polyge conversion's 's sensi- tivity to the threshold (epsilon) is minimal shown in Table varying epsilon from 0.0001 to 0.2x edge length changes Room metrics by <0.01% % other metrics by less than 0.05%, confirming stability and negligible impact on accuracy. This threshold-based approach offers practical trade-off between theoretical rigor and engineering feasibility.
Computational analysis. We analyze the computational cost of our model with respect the number of tokens used in the decoder. The metrics include the number of parameters, floating-poin operations (FLOPs), inference time per image, and GPU memory usage for batch of 10. Figure 13 summarizes these metrics for different token counts. Increasing the number of tokens leads to a higher number parameters and FLOPs, longer inference time, and increased GPU memory consumption. Our method is inherently scalable and built on a general framework. The primary constraint is token length, which is bounded by GPU memory. Scaling is feasible by increasing the token count, though it come with quadratic complexity (O(n?) in both runtime and memory), as is typical for transformer-base models.
Visual Analysis. Figure 14 (main paper) and Figure 15 (appendix) illustrate qualitative differences across ablation settings. Models with edge-basee based representation and denoising exhibit better closure, alignment, and regularity, even under occlusion missing data.
5

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Density Map
GT
RoomFormer
Edge
DN
Edge DN
Edge DN T
3E7E
T4PE
3348
2722
Figure 14: The ablation study on key components. RoomFormer serves as the baseline model. We progressively introduce the Edge module, the Denoising (DN) strategy, and the Swin Twansforffrmmrr- V2 (SwinT) backbone to assess their individual and combined effects.
Density Map
GT
RoomFormer
Edge
DN
Edge DN
Edge DN SwinTT
2222
3292
3366
3262
3270
3452
Figure 15: Additional results for The ablation study on key components. RoomFormer serves as the baseline model. We progressively introduce the Edge module, the Denoising (DN) strategy, and the SwinTransformer-V V2 (Swin')) backbone to assess their individual and combined effects.


Reviews:
[{"review_id": "3NleWOAClh", "reviewer": "Reviewer_SXuu", "summary": "This paper deals with floor-plan reconstruction from 3D point clouds; such a direction is a longstanding goal in the computer vision domain especially for navigation and AR simulation tasks. Instead of relying on a corner-based or line-grouping technique which can be sensitive to noisy data, the authors introduce an edge-based polygon formulation to model each wall as a directed and geometrically continuous edge. CAGE builds a transformer decoder-based denoising framework upon this representation to generate topologically valid room layouts. With experiments on the current publicly available datasets and comparisons with recent state-of-the-art methods, this work makes a strong case for resilience to occlusion and layout noise in floor-plan reconstruction.", "questions": "- As mentioned in the Weaknesses section, I would like to see the answers.\n- Why is there extreme variability in different checkpoints within the same training run? Is this due to convergence related issues of the model?\n- How does multi-room floor-plan estimation work? Some visual examples would be helpful\n- Suggestion: the authors can incorporate training on synthetic datasets such as Aria Synthetic Environments (ASE) to improve generalization precision\n\nI look forward to the authors' responses on the rebuttal and discussion phase.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- Technically well-motivated and simple but efficient change from corner to edge-based representation – maintains directional and structural consistency\n- Polygon matching with edge regression and rasterization losses are intuitive for training a denoising framework in an end-to-end manner (as also shown in the ablation study)\n- Extensive technical validation + comparison with sota methods + detailed limitation analysis\n\nWeaknesses:\n- [Table 2 and 3, main paper] Why is PolyRoom so much better on SceneCAD and cross-dataset generalization precision?\n- [Figure 3, main paper] How sensitive is the method to distance threshold selection for Type IV and others?\n- Any intuition as to why SwinTransformer degrades in performance when used in isolation?\n- Extremely limited runtime analysis – does CAGE have the same speed as RoomFormer? What does lightweight architecture and no need for additional refinement mean? [L261-262]\n- Since the edge-based representation models room layout as fixed-length sequence, how does this scale with complexity of room layout? And, does this affect performance, both in terms of speed and accuracy?\n- [Minor] missing links and references in the draft to figures/tables.", "was_revised": true, "final_justification": "All my and most other reviewers' concerns were clearly addressed in detail in the rebuttal. I keep my score at Accept. I hope the authors will revise the manuscript with the changes from the rebuttal.", "rebuttal": "Thank you for your valuable comments and suggestions. We will revise the paper accordingly and truly appreciate your recognition and encouragement.\n\n**Question 1**: PolyRoom better on SceneCAD and cross-dataset generalization precision?\n\n**Answer**:\n- PolyRoom builds on RoomFormer and leverages a segmentation module (Mask2Former) to generate initial query contours, making it a non-end-to-end method. Its strong performance on SceneCAD—a single-room dataset—largely benefits from the segmentation model, which already provides high-quality priors by the segmentation module. However, this reliance limits its ability to encode multiple-room layouts or perform global reasoning, often leading to room-level conflicts. While PolyRoom shows higher precision due to its dependence on segmentation quality, our method achieves superior recall and F1 on key metrics (Room IoU, Corner Recall, Angle Recall, and Angle F1), highlighting stronger generalization and structural robustness.\n\n**Question 2**: How sensitive is the method to distance threshold selection for Type IV and others?\n\n**Answer**:\n- The edge-to-polygon convertion is empirically stable with respect to the threshold. Type I intersections account for 97.06%, while Types II–IV cover 1.17%, 0.47%, and 1.29%. Shown in the following table, varying the threshold (0.0001 to 0.2× edge length) changes metrics by less than 0.05%, confirming robustness to the epsilon value.\n\n| eps     | Room Prec. | Room Rec. | Room F1 | Corner Prec. | Corner Rec. | Corner F1 | Angle Prec. | Angle Rec. | Angle F1 |\n|---------|------------|-----------|---------|--------------|-------------|-----------|-------------|------------|----------|\n| 0.2     | 99.59      | 98.69     | 99.14   | 95.04        | 88.57       | 91.70     | 92.57       | 86.40      | 89.39    |\n| 0.1     | 99.59      | 98.69     | 99.14   | 94.99        | 88.55       | 91.66     | 92.51       | 86.37      | 89.34    |\n| 0.05    | 99.59      | 98.69     | 99.14   | 94.96        | 88.55       | 91.65     | 92.46       | 86.34      | 89.30    |\n| 0.01    | 99.59      | 98.69     | 99.14   | 94.95        | 88.58       | 91.66     | 92.40       | 86.34      | 89.27    |\n| 0.0001  | 99.59      | 98.69     | 99.14   | 94.94        | 88.60       | 91.67     | 92.38       | 86.34      | 89.26    |\n\n**Question 3**: Any intuition as to why SwinTransformer degrades in performance when used in isolation?\n\n**Answer**:\n- In our experiments, SwinTransformer captures fine details but is more sensitive to noise, often resulting in irregular or self-intersecting polygons when used directly in the RoomFormer framework. In contrast, our method better leverages Swin’s global reasoning through the edge-based representation and dual-query strategy, enhancing robustness and yielding more stable and accurate layouts.\n\n**Question 4**: Extremely limited runtime analysis – does CAGE have the same speed as RoomFormer? What does \"lightweight architecture and no need for additional refinement\" mean? [L261-262]\n\n**Answer**:\n- CAGE has 40.96M parameters with a ResNet-50 backbone (vs. RoomFormer’s 40.60M), converges stably (650 epochs on Structured3D, 400 on SceneCAD), and achieves fast inference (~0.01s/image), matching RoomFormer and outperforming other baselines in speed. We will include a detailed runtime analysis in the revised version.\n- The phrase “lightweight architecture and no need for additional refinement” refers to our advantage over all other methods except RoomFormer. CAGE only performs a simple edge-to-polygon conversion, making it faster than models requiring complex refinement or optimization.\n\n**Question 5**: Since the edge-based representation models room layout as fixed-length sequence, how does this scale with complexity of room layout? And, does this affect performance, both in terms of speed and accuracy?\n\n**Answer**:\n- We use a configuration of 800 tokens (40 polygons × 20 edges), which is sufficient for most residential layouts. For example, Structured3D typically contains fewer than 10 rooms per floor, and SceneCAD includes only one room per case. As demonstrated in case 3410 (Fig. 10, supplementary), our model handles complex multi-room layouts effectively.\n- Our method is inherently scalable and built on a general framework. The primary constraint is token length, which is bounded by GPU memory. Scaling is feasible by increasing the token count, though it come with quadratic complexity (O(n²) in both runtime and memory), as is typical for transformer-based models.\n- For very larger scenes, our approach can be naturally extended by tiling the input into smaller subregions and merging the outputs—a widely used and effective strategy in applications such as cadastral mapping and topographic reconstruction.\n\n| Number of Tokens | Params(M) | FLOPs(G) | inference time (ms/image) | 10*GPU（1Batch） |\n|------------------|-----------|----------|---------------------------|-----------------|\n| 1200             | 41.1      | 26.4     | 4.7                       | 12.6            |\n| 1400             | 41.1      | 29.7     | 5.5                       | 15.9            |\n| 1600             | 41.2      | 33.2     | 6.1                       | 19.8            |\n| 1800             | 41.2      | 37.0     | 7.2                       | 24.1            |\n| 2000             | 41.3      | 41.1     | 8.1                       | 28.8            |\n| 2200             | 41.3      | 45.4     | 9.1                       | 34.1            |\n| 2400             | 41.4      | 50.0     | 10.2                      | 39.8            |\n| 2600             | 41.4      | 54.9     | 11.5                      | 46.0            |\n| 2800             | 41.5      | 60.0     | 12.8                      | 52.6            |\n| 3000             | 41.5      | 65.4     | 14.2                      | 59.8            |\n| 3200             | 41.6      | 71.0     | 15.6                      | 67.4            |\n\n**Question 6**: Why is there extreme variability in different checkpoints within the same training run? Is this due to convergence related issues of the model?\n\n**Answer**:\n- We observed some variability across checkpoints, likely due to stochastic elements such as dropout. However, these fluctuations are minor, and overall convergence is stable. We repeatedly verified the experiments, and the results reported in the paper are consistent and robust across runs.\n\n**Question 7**: How does multi-room floor-plan estimation work? Some visual examples would be helpful.\n\n**Answer**:\n- We adopt a two-level token structure where tokens 0–m represent the first polygon, m+1–2m the second, and so on, allowing structured and efficient multi-room representation. We will include additional visual examples to better illustrate this in the revised version.\n\n**Question 8**: Suggestion: the authors can incorporate training on synthetic datasets such as Aria Synthetic Environments (ASE) to improve generalization precision.\n\n**Answer**:\n- Thank you for the suggestion. ASE’s large-scale synthetic dataset (100K scenes, ~28× larger than Structured3D) is indeed valuable. We are actively collecting diverse real and synthetic scenes, including ASE, to further enhance generalization in future extensions.\n\n**Question 9**: Typos and Mis-links: \n\n**Answer**:\n- Thank you for pointing these out. We will correct all typos and fix the broken references in the revised version."}, {"review_id": "KlU7gALqmx", "reviewer": "Reviewer_EK67", "summary": "This paper presents CAGE, an end-to-end framework for vectorized floorplan reconstruction from 3D point clouds. The key innovation lies in its edge-centric representation, where each wall is modeled as a directed and geometrically continuous edge, as opposed to traditional corner-based approaches. This design aims to improve robustness under occlusion and noisy input by explicitly encoding structural continuity. The authors introduce a dual-query transformer decoder that integrates both latent and perturbed edge queries within a denoising framework, enabling stable training and iterative polygon refinement. Experiments on Structured3D and SceneCAD demonstrate that CAGE achieves state-of-the-art performance across room, corner, and angle-level metrics, and shows strong cross-dataset generalization.", "questions": "See weaknesses", "limitations": "See weaknesses", "rating": 5, "confidence": 1, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**:\n- The paper is well-structured and clearly written. It introduces a strong motivation for moving from corner-based to edge-based representations in floorplan reconstruction, and presents the approach in a logically coherent manner with helpful visual illustrations.\n- The proposed edge-centric formulation enables topologically consistent and robust polygon reconstruction, especially under partial occlusion or missing data—an important challenge in real-world scans.\n- The use of a dual-query transformer decoder with denoising supervision is both novel and effective. It improves convergence and prediction stability, and is well-supported by ablation studies and competitive results on Structured3D and SceneCAD.\n- The method achieves state-of-the-art performance on several key metrics (Room, Corner, Angle) and demonstrates strong cross-dataset generalization without post-processing.\n\n**Weaknesses**:\n- Although the paper emphasizes robustness under occlusion and clutter, it lacks controlled evaluation under extreme occlusion scenarios (e.g., dense furniture occluding multiple walls). A more detailed analysis of failure cases would strengthen the empirical claims.\n- The paper does not evaluate performance on datasets with complex geometries such as curved walls or highly non-Manhattan layouts, despite claiming general applicability. While the authors mention such diversity in Structured3D, there is no specific analysis or visualization to confirm robustness in these cases.\n- I am not an expert in floorplan reconstruction, but it seems that the edge-based representation—while robust—might struggle to capture very short wall segments or fine structural details compared to corner-based methods. This tradeoff is not discussed.\n- Similarly, the paper does not report model size, parameter count, or compute time beyond per-frame inference speed. From a system perspective, it would be helpful to better understand the computational efficiency. Again, this is an intuition rather than a domain-specific concern.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable comments and suggestions. We will revise the paper accordingly and truly appreciate your recognition and encouragement.\n\n**Question 1**:  Robustness under extreme occlusion\n\n**Answer**:\n- We evaluated CAGE under severe occlusion scenarios, highlighted by red circles in Fig. 6 and Fig. 11 (supplementary). E.g., in Scene 3292, the living room has two fully unscanned walls and one partially visible wall due to the absence of a scanner. In Scene 3356, two walls in the bottom-right room are unscanned, and one wall in the top-right bedroom is fully blocked by a wardrobe. Despite these challenges, CAGE reconstructs complete room polygons, showing strong robustness where corner-based methods often fail.\n\n**Question 2**: Complex geometries\n\n**Answer**:\n- CAGE is not limited to Manhattan layouts or flat surfaces. As highlighted in red circles in Scene 3493 (Fig. 6) and Scene 3443 (Fig. 10), as well as in many cases in Figs. 7–8, CAGE handles slanted walls by predicting edges at arbitrary angles and approximates curved structures using short segments, similar to other polygon-based methods. While Structured3D and SceneCAD do not contain curved geometry, cross-dataset testing on our collected data confirms CAGE’s generalizability. We will include examples of curved walls in the supplementary material of the revised version.\n\n**Question 3**:  Short wall segments\n\n**Answer**:\n- CAGE captures short wall segments and fine structures effectively, including alcoves and dividers (e.g., the living room in Fig. 6). Its edge-based representation, combined with global reasoning, reduces the risk of missing details that corner-based methods often overlook. Even at 256×256 resolution, CAGE remains robust to noise and occlusion, with potential for further gains at higher resolutions.\n\n**Question 4**:  Computational efficiency\n\n**Answer**:\n- Thank you for the suggestion. We will report full efficiency details in the revised manuscript. CAGE has 40.96M parameters with a ResNet-50 backbone (vs. RoomFormer’s 40.60M), converges stably (650 epochs on Structured3D, 400 on SceneCAD), and achieves fast inference (~0.01s/image), matching RoomFormer and outperforming other baselines in speed."}, {"review_id": "c3CpjIWofv", "reviewer": "Reviewer_fJqK", "summary": "This paper proposes a method, called CAGE to reconstruct vector floorplans from point cloud density maps. CAGE builds on top of RoomFormer and mainly introduces two designs: (1) Modify the corner representation of each query in RoomFormer to edge representation (i.e. two end points). (2) Add a denoising (DN) training strategy. In addition, the paper explored the importance of backbones (ResNet vs SwinTransformer) in floorplan reconstruction. After integrating all the designs, it attains state-of-the-art results on Structured3D and near-sota results on SceneCAD.", "questions": "- I look forward to the authors’ response on weakness 1.\n\nMore questions:\n\n- In the edge representation, each query represents two end-points of one edge.\n    - How do you decide the order of the two end-points in this edge, i.e. which one is the start/end?\n    - Do you think the edge representation actually encodes redundant information? Because two adjacent edges share the common point, that means one point is encoded twice in two edges.\n    - As shown in Figure 3, the network cannot predict perfectly shared vertice of two adjacent edges (thus manual merging is needed). Curious question/suggestion: will adding some explicit regularization loss, e.g. forcing the predicted shared vertice of two adjacent edges to be the same, help?\n- In tab 4 in the supplementary material, the paper shows incremental ablation on top of RoomFormer. However, it was mentioned that “RoomFormer [12] results are reproduced using our training setup for fair comparison”. But RoomFormer results here are actually worse than in tab. 1. This looks strange to me. Since the proposed method is built on top of RoomFormer, why didn’t you use their training setup to have a strong staring point?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths:**\n\n- **The most interesting and novel part is that the paper proposes edge-centric presentation to model floorplan. The motivation makes sense to me.** Compared with RoomFormer’s corner representation, where direction (or connectivity) of corners is implicitly learned using attention, the edge representation encodes directionality explicitly and promotes angular regularity (also reflected on the big improvements on angle metrics). However, I think this representation also has its own limitation, see weaknesses part.\n- In addition to regular reconstruction loss adopted from RoomFormer, this paper proposes to add a denoising training objective, where some controlled noises are added to the ground truth and let the network to learn to denoise it. Although this idea is adopted from DN-DETR, **it is interesting to see this strategy is proven beneficial in the floorplan reconstruction task**.\n- The paper conducts ablation studies on the two core designs (edge representation and denoising loss) and backbones, **making it clear where the improvements come from**.\n- The paper conducts comprehensive comparison with previous methods and achieves state-of-the-art results on Structured3D and near-sota results on SceneCAD.\n- The paper contains nice and clear figures, which helps illustrate the designs. The writing is clear and easy to follow.\n\n**Weaknesses:**\n\n- The paper proposes edge-centric representation. Although it is proven superior, **I think the limitation is that it requires heuristics-driven post-processing to merge the predicted edges to final polygons (Figure 3)**. By contrast, RoomFormer directly predicts ordered corner sequences, forming polygons without any post-processing. **In that sense, the proposed edge representation is not elegant to me and I don’t think the paper can claim itself end-to-end floorplan reconstruction anymore, since it actually requires post-processing**.\n- This paper conducts ablation studies but puts all of the tables and detailed discussion in the supplementary. However, **I think those experimental results are very important and they should be placed in the main paper**.\n- In addition to the two main designs, another difference between the proposed method and RoomFormer is the backbone network. For tab.1 and tab. 2, **I suggest to add another column to indicate the backbone (and number of parameters) of each method. This will make the comparison more fair**.\n- Typos:\n    - L118: Figure 4(c) → Figure 2(c)\n    - L244, L301, L302, L310: “??” missed reference.\n    - L528: (main paper) → (appendix)\n    - L260: fasted", "was_revised": true, "final_justification": "Thank the authors for the detailed and clear rebuttal, which addressed almost all of my concerns. Therefore, I would like to increase my rating from \"Borderline accept\" to \"Accept\". I hope the authors will revise the paper accordingly as what they promised in the rebuttal.", "rebuttal": "Thank you for your valuable comments and suggestions. We will revise the paper accordingly and truly appreciate your recognition and encouragement.\n\n**Question 1**:  Edge-centric representation and post-processing\n\n**Answer**:\n- Thank you for raising this point. While CAGE performs a lightweight edge-to-polygon conversion, it is not an optimization based post-processing step in the traditional sense. The transformer decoder directly predicts directed edge sequences that form closed loops, and the conversion simply connects these edges into polygons deterministically. It does not involve learning, optimization, or iterative refinement.\n- Importantly, this conversion is highly robust: in the Structured3D dataset, over 98.71% of intersections are of Types I–III (i.e., directionally valid and unambiguous). The conversion's sensitivity to the threshold (epsilon) is minimal—as shown in the table below, varying epsilon from 0.0001 to 0.2× edge length changes Room metrics by <0.01% and other metrics by less than 0.05%, confirming stability and negligible impact on accuracy. This threshold-based approach offers a practical trade-off between theoretical rigor and engineering feasibility.\n\n| eps     | Room Prec. | Room Rec. | Room F1 | Corner Prec. | Corner Rec. | Corner F1 | Angle Prec. | Angle Rec. | Angle F1 |\n|---------|------------|-----------|---------|--------------|-------------|-----------|-------------|------------|----------|\n| 0.2     | 99.59      | 98.69     | 99.14   | 95.04        | 88.57       | 91.70     | 92.57       | 86.40      | 89.39    |\n| 0.1     | 99.59      | 98.69     | 99.14   | 94.99        | 88.55       | 91.66     | 92.51       | 86.37      | 89.34    |\n| 0.05    | 99.59      | 98.69     | 99.14   | 94.96        | 88.55       | 91.65     | 92.46       | 86.34      | 89.30    |\n| 0.01    | 99.59      | 98.69     | 99.14   | 94.95        | 88.58       | 91.66     | 92.40       | 86.34      | 89.27    |\n| 0.0001  | 99.59      | 98.69     | 99.14   | 94.94        | 88.60       | 91.67     | 92.38       | 86.34      | 89.26    |\n\n**Question 2**:  Ablation studies in supplementary\n\n**Answer**:\n- We appreciate your suggestion and will move the key ablation results from Appendix B.5 to the main paper to clearly highlight the impact of edge representation and denoising decoder.\n\n**Question 3**:  Backbone network comparison\n\n**Answer**:\n- Thanks for your suggestion. We will clarify this in the paper and include each model’s backbone and parameter count in Tables 1 and 2. All our baseline results (for RoomFormer and others) use a ResNet-50 backbone. Our method also uses ResNet-50 in these comparisons. CAGE has 40.96M parameters with a ResNet-50 backbone (comparable to RoomFormer’s 40.60M) and 210M with Swin Transformer V2. Despite this, its lightweight architecture enables fast inference (~0.01s/image), matching RoomFormer and outperforming other baselines in speed.\n\n**Question 4**:  Edge endpoint order\n\n**Answer**:\n- We enforce a consistent edge ordering by starting from the top-left corner of each room polygon, listing edges counter-clockwise, with the first endpoint as the starting point. This sequence is used throughout training to ensure consistency.\n\n**Question 5**:  Redundancy in edge representation\n\n**Answer**:\n- Thank you for the insightful suggestion. We experimented with an explicit co-point regularization loss that encourages adjacent edges to share endpoints. The results (see table below) show modest gains in most metrics, particularly for corner and angle F1 scores, though room recall slightly drops. Without this term, our vertex coordinate loss already implicitly promotes shared vertices by aligning predicted edges to ground truth. We will explore this mechanism behind the interesting result in future work.\n- Notably, the explicit regularization improves learning but cannot guarantee exact vertex alignment due to its soft constraint—so edge intersection is still needed during edge-to-polygon conversion.\n- We do not consider the edge representation redundant. Each edge encodes position, direction, and length—information not explicitly available in corner-based approaches. This rich geometric encoding enhances global layout reasoning and precision under occlusion.\n\n| Co-Point | Room Prec. | Room Rec. | Room F1 | Room Prec. | Room Rec. | Room F1 | Room Prec. | Room Rec. | F1 |\n|----------|------------|-----------|---------|--------------|-------------|-----------|-------------|------------|----------|\n| ✗        | 99.59      | 98.69     | 99.23   | 93.88        | 88.79       | 91.26     | 89.65       | 84.88      | 87.20    |\n| ✓        | 99.92      | 98.56     | 99.24   | 93.95        | 89.32       | 91.58     | 89.78       | 85.42      | 87.55    |\n\n**Question 6**: RoomFormer results discrepancy\n\n**Answer**:\n- We reproduced RoomFormer using its official code, hyperparameters, and data splits on our hardware. Slight differences are expected due to implementation environments, but the setup ensures fair ablation comparisons. As shown below, our full model significantly outperforms both the original RoomFormer and our replicated baseline (*), validating the effectiveness of our contributions.\n\n| Method      | Room Prec. | Room Rec. | Room F1 | Corner Prec. | Corner Rec. | Corner F1 | Angle Prec. | Angle Rec. | Angle F1 |\n|-------------|------------|-----------|---------|--------------|-------------|-----------|-------------|------------|----------|\n| RoomFormer  | 97.9       | 96.7      | 97.3    | 89.2         | 85.3        | 87.2      | 83.0        | 79.6       | 81.3     |\n| RoomFormer* | 96.9       | 95.9      | 96.4    | 87.3         | 83.8        | 85.5      | 81.2        | 77.9       | 79.5     |\n| Ours(full)  | 99.6       | 98.7      | 99.1    | 95.0         | 88.6        | 91.7      | 92.5        | 86.4       | 89.3     |\n\n**Question 7**:  Typos and Mis-links\n\n**Answer**:\n- Thank you for pointing these out. We will correct all typos and fix the broken references in the revised version."}, {"review_id": "D5vsgFROIE", "reviewer": "Reviewer_yHWf", "summary": "The paper addresses the problem of floor plan prediction from a point cloud, i.e., estimate 2D polygons that fit rooms' layout.\nThe proposed method belongs to the category of methods where the point cloud is pre-processed into a 2D ground density map by projecting the point cloud onto the \"ground\", which is fed to a network that outputs the floor plan room / polygons.\n\nThe contribution of the paper lies in the choice network architecture and training representations:\n- polygon are represented by their edges, which detection fails more gracefully than the vertex-based polygon representations used in previous work\n- the network is a DN-DETR transformer network\n\nThe experiments follow the standard guidelines to evaluate floorplan reconstruction (datasets, metrics) and the baselines are relevant.\nThe results show that the proposed method achieves better floorplan reconstruction and supports the relevance of the proposed method.\n\nThe paper is closely related to RoomFormer  with the following differences:\n- RoomFormer has a vertex-based representation whereas CAGE has an edge-based one\n- CAGE adopts DN-DETR instead of RoomFormer's DETR", "questions": "There is no questions as the paper is very well presented.", "limitations": "No, the authors have not adequately addressed the limitations of their work.\nSome limitations worth mentioning are:\n-  the maximum number of room and the maximum number of edges per room are harcoded in the network so any deployment to spaces with more rooms or room with more edges would require retraining\n- the size of the floorplan density map is fixed to 256 x 256 pixels so the network can probably not scale to large spaces as the density map's resolution would decrease, but this is a limitation common to all methods and can be mitigated, e.g.,  by splitting the space into more smaller areas.\n \nThere is no potential negative societal impact in this work.\n\n### Misc comments.\n\nA few typos:\n- L118: Fig4 -> Fig3\n- L160: \"N the number of edges\" -> \"N the maximum number of edges\"\n- L199: \"dummy\" -> \"mock\"\n- L243: \"Adam\" optimizer is missing citation\n- L250: \"fasted\" -> \"faster\"\n- A few undefined references to supp. material marked with \"??\"", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 2, "strengths_and_weaknesses": "- S1 - The choice of architecture, geometric representation and losses is sound.\n\n- S2 - The related work is exhaustive although it does not highlight the difference with the proposed method enough.\n\n- S3 - The paper is extremely well written, clear and pleasant to read.\n\n- S4 - The experiments follow the standard (datasets, metrics, baseline) and support the relevance of the method.\n\n- W1 - The contribution with respect to RoomFormer is too low for a Neurips submission: the differences are the change of decoder architecture (CAGE adopts the existing DN-DETR instead of RoomFormer's DETR) and the representation of the polygons by their edges. This is what motivates the low rating of the paper.", "was_revised": true, "final_justification": "Thank you for the rebuttal and for clarifying the importance of the contributions.\nAlthough the paper demonstrates that edges are a sound representation for floor-plan reconstruction, the paper remains very close to RoomFormer, which motivates the rating update from reject to only borderline accept.", "rebuttal": "Thank you for your constructive feedback. We will carefully revise the paper to address your concerns and sincerely appreciate your thoughtful review and critical insights.\n\n**Question 1**:  Contribution with respect to RoomFormer\n\n**Answer**:\n- CAGE introduces two key contributions: (1) an edge-based polygon representation that explicitly encodes geometry and direction, enhancing robustness to occlusion and missing corners (e.g., +7.7% Angle F1); and (2) a dual-query denoising transformer decoder, which extends DN-DETR to sequential polygon prediction via perturbed edge queries. Ablation results (Appendix B.5) show that removing either component leads to significant drops (–5.8% Corner F1, –1.9% Angle F1).\n- This is far from a trivial extension. Our edge-centric formulation directly addresses core limitations of corner-based approaches and ensures topological consistency. As recognized by R#fJqK, \"the edge representation encodes directionality explicitly and promotes angular regularity… beneficial in floorplan reconstruction.\" R#Sxuu called it a \"simple but efficient change… maintaining directional and structural consistency.\" Besides, R#EK67 affirmed that \"The dual-query decoder with denoising supervision is both novel and effective… robust under occlusion.\" Notably, while DN-DETR is an existing method, we are the first to adapt it for floorplan reconstruction task. By leveraging denoising supervision, we obviously enhance both accuracy and stability, making this a highly valuable contribution.\n- Together, these innovations yield significant accuracy and robustness gains. We establish a foundation for broader raster-to-vector tasks, such as façade modeling and cadastral map reconstruction, where we are actively extending our framework.\n\n**Question 2**:  Hardcoded maximum number of rooms and edges\n\n**Answer**:\n- Our method is inherently scalable and built on a general framework, with the primary constraint being token length, which is limited by available GPU memory. We use a configuration of 800 tokens (40 polygons × 20 edges), which is sufficient for most residential layouts. For example, Structured3D typically contains fewer than 10 rooms per suit, and SceneCAD includes only one room per case. As demonstrated in case 3410 (Fig. 10, supplementary), our model handles complex multi-room layouts effectively.\n- For larger scenes, our approach can be naturally extended by tiling the input into smaller subregions and merging the outputs—an effective strategy in applications such as cadastral mapping and topographic reconstruction. This allows flexible scaling without requiring retraining or modification of the core architecture.\n\n**Question 3**:  Fixed floorplan density map size\n\n**Answer**:\n- We appreciate the suggestion regarding image tiling. The fixed 256×256 resolution is a common design choice in generative models and has proven sufficient for typical residential layouts in datasets like Structured3D and SceneCAD. For larger or more complex spaces, the resolution can be increased or the floorplan can be tiled into smaller subregions—both are practical, widely used strategies that preserve the core architecture.\n\n**Question 4**:  Typos and Mis-links\n\n**Answer**:\n- Thank you for pointing these out. We will correct all typos and fix the broken references in the revised manuscript."}]

Author rebuttal:

Meta-review: The paper describes a new system called CAGE (Continuity-Aware edge) for floor-plan reconstruction from a 3D point cloud (via projection onto a 2D density map). Walls are modeled as geometrically continuous edges, with each room represented by a (watertight) polygon. The architecture is based on a dual-query transformer decoder. 

Pros: The paper is well-written, with the novelty being the robust edge-centric approach to modeling floorplan. While there are ideas adapted from RoomFormer and DN-DETR, the unique design decisions are highly effective in generating SOTA (or near SOTA) results for Structured3D and SceneCAD. 

Cons: The system is not exactly end-to-end because post-processing is required. In addition, the maximum number of rooms and edges per room are hardcoded, which limits practicality of the system. 

The novelty in representing the floorplan and architectural design decisions as well as the SOTA results are the main reasons for acceptance. There is consensus among the reviewers for accepting this paper.
Decision: Accept (poster)