CAGE: Continuity-Aware edGE Network Unlocks Robust Floorplan Reconstruction

• URL: http://arxiv.org/abs/2509.15459v2
• Date: Tue, 14 Oct 2025 20:13:58 GMT
• Title: 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,
• Abstract summary: We present CAGE, a robust framework for reconstructing vector floorplans directly from point-cloud density maps.<n>CAGE achieves state-of-the-art performance, with F1 scores of 99.1% (rooms), 91.7% (corners), and 89.3% (angles)
• Score: 24.09888364478496
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• StepVAR: Structure-Texture Guided Pruning for Visual Autoregressive Models [98.72926158261937]
  We propose a training-free token pruning framework for Visual AutoRegressive models.<n>We employ a lightweight high-pass filter to capture local texture details, while leveraging Principal Component Analysis (PCA) to preserve global structural information.<n>To maintain valid next-scale prediction under sparse tokens, we introduce a nearest neighbor feature propagation strategy.
  arXiv  Detail & Related papers  (2026-03-02T11:35:05Z)
• FACE: A Face-based Autoregressive Representation for High-Fidelity and Efficient Mesh Generation [50.71369329585773]
  We introduce FACE, a novel Autoregressive Autoencoder framework that generates meshes at the face level.<n>Our one-face-one-token strategy treats each triangle face, the fundamental building block of a mesh, as a single, unified token.<n> FACE achieves state-of-the-art reconstruction quality on standard benchmarks.
  arXiv  Detail & Related papers  (2026-03-02T06:47:15Z)
• Raster2Seq: Polygon Sequence Generation for Floorplan Reconstruction [21.366278792227785]
  We propose Raster2Seq, framing floorplan reconstruction as a sequence-to-sequence task.<n>An autoregressive decoder learns to predict the next corner conditioned on image features and previously generated corners.<n>Our method achieves state-of-the-art performance on standard benchmarks.
  arXiv  Detail & Related papers  (2026-02-09T18:58:46Z)
• FloorplanVLM: A Vision-Language Model for Floorplan Vectorization [15.691267151619442]
  We present FloorplanVLM, a unified framework that reformulates vectorization as an image-conditioned sequence modeling task.<n>This 'pixels-to-sequence' paradigm enables the precise and holistic constraint satisfaction of complex grounding, such as walls and curved arcs.
  arXiv  Detail & Related papers  (2026-02-06T08:57:52Z)
• ControlVP: Interactive Geometric Refinement of AI-Generated Images with Consistent Vanishing Points [32.23473666846317]
  We propose ControlVP, a user-guided framework for correcting vanishing point inconsistencies in generated images.<n>Our approach extends a pre-trained diffusion model by incorporating structural guidance derived from building contours.<n>Our method enhances global geometric consistency while maintaining visual fidelity comparable to the baselines.
  arXiv  Detail & Related papers  (2025-12-08T12:38:11Z)
• Enhancing Floor Plan Recognition: A Hybrid Mix-Transformer and U-Net Approach for Precise Wall Segmentation [0.0]
  This study introduces MitUNet, a hybrid neural network combining a Mix-Transformer encoder and a U-Net decoder.<n>Our approach achieves a balance between precision and recall, ensuring accurate boundary recovery.<n> Experiments on the CubiCasa5k dataset and a proprietary regional dataset demonstrate MitUNet's superiority in generating structurally correct masks.
  arXiv  Detail & Related papers  (2025-12-02T04:47:53Z)
• TALO: Pushing 3D Vision Foundation Models Towards Globally Consistent Online Reconstruction [57.46712611558817]
  3D vision foundation models have shown strong generalization in reconstructing key 3D attributes from uncalibrated images through a single feed-forward pass.<n>Recent strategies align consecutive predictions by solving global transformation, yet our analysis reveals their fundamental limitations in assumption validity, local alignment scope, and robustness under noisy geometry.<n>We propose a higher-DOF and long-term alignment framework based on Thin Plate Spline, leveraging globally propagated control points to correct spatially varying inconsistencies.
  arXiv  Detail & Related papers  (2025-12-02T02:22:20Z)
• Rethinking Multimodal Point Cloud Completion: A Completion-by-Correction Perspective [8.276620253870338]
  Point cloud completion aims to reconstruct complete 3D shapes from partial observations.<n>Most methods still follow a Completion-by-Inpainting paradigm.<n>We propose Completion-by-Correction, which begins with a complete shape prior and performs feature-space correction to align it with the partial observation.
  arXiv  Detail & Related papers  (2025-11-15T11:51:13Z)
• Light-SQ: Structure-aware Shape Abstraction with Superquadrics for Generated Meshes [60.92139345612904]
  We present Light-SQ, a novel superquadric-based optimization framework.<n>We propose a block-regrow-fill strategy guided by structure-aware volumetric decomposition.<n>Experiments demonstrate that Light-SQ enables efficient, high-fidelity, and editable shape abstraction with superquadrics.
  arXiv  Detail & Related papers  (2025-09-29T16:18:32Z)
• Seeing 3D Through 2D Lenses: 3D Few-Shot Class-Incremental Learning via Cross-Modal Geometric Rectification [59.17489431187807]
  We propose a framework that enhances 3D geometric fidelity by leveraging CLIP's hierarchical spatial semantics.<n>Our method significantly improves 3D few-shot class-incremental learning, achieving superior geometric coherence and robustness to texture bias.
  arXiv  Detail & Related papers  (2025-09-18T13:45:08Z)
• Geometry-Editable and Appearance-Preserving Object Compositon [67.98806888489385]
  General object composition (GOC) aims to seamlessly integrate a target object into a background scene with desired geometric properties.<n>Recent approaches derive semantic embeddings and integrate them into advanced diffusion models to enable geometry-editable generation.<n>We introduce a Disentangled Geometry-editable and Appearance-preserving Diffusion model that first leverages semantic embeddings to implicitly capture desired geometric transformations.
  arXiv  Detail & Related papers  (2025-05-27T09:05:28Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• CornerFormer: Boosting Corner Representation for Fine-Grained Structured Reconstruction [20.04081992616026]
  We present an enhanced corner representation method for structured reconstruction. It better reconstructs fine-grained structures, such as adjacent corners and tiny edges. It outperforms the state-of-the-art model by +1.9%@F-1 on Corner and +3.0%@F-1 on Edge.
  arXiv  Detail & Related papers  (2023-04-14T11:51:26Z)
• Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis [66.49788145564004]
  We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology. Our methods perform favorably against the current state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-12-17T15:05:25Z)
• Neural 3D Scene Reconstruction with the Manhattan-world Assumption [58.90559966227361]
  This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images. Planar constraints can be conveniently integrated into the recent implicit neural representation-based reconstruction methods. The proposed method outperforms previous methods by a large margin on 3D reconstruction quality.
  arXiv  Detail & Related papers  (2022-05-05T17:59:55Z)
• Quantization in Relative Gradient Angle Domain For Building Polygon Estimation [88.80146152060888]
  CNN approaches often generate imprecise building morphologies including noisy edges and round corners. We propose a module that uses prior knowledge of building corners to create angular and concise building polygons from CNN segmentation outputs. Experimental results demonstrate that our method refines CNN output from a rounded approximation to a more clear-cut angular shape of the building footprint.
  arXiv  Detail & Related papers  (2020-07-10T21:33:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.