Title: CAGE: Continuity-Aware edGE Network Unlocks Robust Floorplan Reconstruction
Venue: NeurIPS 2025
Accepted: yes
Keywords: floorplan reconstruction

continuity-aware edge

representation learning

point clouds

density map
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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\n\nSTRENGTHS AND WEAKNESSES:\n- 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.\n\nQUESTIONS:\nThere is no questions as the paper is very well presented.\n\nLIMITATIONS:\nNo, 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 \"??\"", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-08"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\nSee weaknesses\n\nLIMITATIONS:\nSee weaknesses", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 1, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}