FRI-Net: Floorplan Reconstruction via Room-wise Implicit Representation

• URL: http://arxiv.org/abs/2407.10687v1
• Date: Mon, 15 Jul 2024 13:01:44 GMT
• Title: FRI-Net: Floorplan Reconstruction via Room-wise Implicit Representation
• Authors: Honghao Xu, Juzhan Xu, Zeyu Huang, Pengfei Xu, Hui Huang, Ruizhen Hu,
• Abstract summary: We introduce a novel method called FRI-Net for 2D floorplan reconstruction from 3D point cloud. By incorporating geometric priors of room layouts in floorplans into our training strategy, the generated room polygons are more geometrically regular. Our method demonstrates improved performance compared to state-of-the-art methods, validating the effectiveness of our proposed representation for floorplan reconstruction.
• Score: 18.157827697752317
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce a novel method called FRI-Net for 2D floorplan reconstruction from 3D point cloud. Existing methods typically rely on corner regression or box regression, which lack consideration for the global shapes of rooms. To address these issues, we propose a novel approach using a room-wise implicit representation with structural regularization to characterize the shapes of rooms in floorplans. By incorporating geometric priors of room layouts in floorplans into our training strategy, the generated room polygons are more geometrically regular. We have conducted experiments on two challenging datasets, Structured3D and SceneCAD. Our method demonstrates improved performance compared to state-of-the-art methods, validating the effectiveness of our proposed representation for floorplan reconstruction.

Related papers

• PolyRoom: Room-aware Transformer for Floorplan Reconstruction [17.154556344393743]
  We present PolyRoom, a room-aware Transformer to reconstruct floorplans from point clouds. Specifically, we adopt a uniform sampling floorplan representation to enable dense supervision during training and effective utilization of angle information. Results on two widely used datasets demonstrate that PolyRoom surpasses current state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2024-07-15T04:53:10Z)
• 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)
• Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries [27.564355569013706]
  We develop a novel Transformer architecture that generates polygons of multiple rooms in parallel. Our method achieves a new state-of-the-art for two challenging datasets, Structured3D and SceneCAD. It can readily be extended to predict additional information, i.e., semantic room types and architectural elements like doors and windows.
  arXiv  Detail & Related papers  (2022-11-28T18:59:09Z)
• Single-view 3D Mesh Reconstruction for Seen and Unseen Categories [69.29406107513621]
  Single-view 3D Mesh Reconstruction is a fundamental computer vision task that aims at recovering 3D shapes from single-view RGB images. This paper tackles Single-view 3D Mesh Reconstruction, to study the model generalization on unseen categories. We propose an end-to-end two-stage network, GenMesh, to break the category boundaries in reconstruction.
  arXiv  Detail & Related papers  (2022-08-04T14:13:35Z)
• Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes [50.317223783035075]
  We present a new framework to reconstruct holistic 3D indoor scenes from single-view images. We propose an instance-aligned implicit function (InstPIFu) for detailed object reconstruction. Our code and model will be made publicly available.
  arXiv  Detail & Related papers  (2022-07-18T14:54:57Z)
• MCTS with Refinement for Proposals Selection Games in Scene Understanding [32.92475660892122]
  We propose a novel method applicable in many scene understanding problems that adapts the Monte Carlo Tree Search (MCTS) algorithm. From a generated pool of proposals, our method jointly selects and optimize proposals that maximize the objective term. Our method shows high performance on the Matterport3D dataset without introducing hard constraints on room layout configurations.
  arXiv  Detail & Related papers  (2022-07-07T10:15:54Z)
• Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshes [52.038346313823524]
  This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology. Our method is able to produce high-quality meshes, particularly with diverse topologies, as compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-10T08:32:57Z)
• 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)
• Black-Box Test-Time Shape REFINEment for Single View 3D Reconstruction [57.805334118057665]
  We propose REFINE, a postprocessing mesh refinement step that can be easily integrated into the pipeline of any black-box method in the literature. At test time, REFINE optimize a network per mesh instance, to encourage consistency between the mesh and the given object view.
  arXiv  Detail & Related papers  (2021-08-23T03:28:47Z)
• Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image [32.5277483805739]
  Single-image room layout reconstruction aims to reconstruct the enclosed 3D structure of a room from a single image. This paper considers a more general indoor assumption, i.e., the room layout consists of a single ceiling, a single floor, and several vertical walls.
  arXiv  Detail & Related papers  (2021-04-16T09:24:08Z)
• MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans [41.31546857809168]
  We propose a novel method for reconstructing floor plans from noisy 3D point clouds. Our main contribution is a principled approach that relies on the Monte Carlo Tree Search (MCTS) algorithm. We evaluate our method on the recent and challenging Structured3D and Floor-SP datasets.
  arXiv  Detail & Related papers  (2021-03-20T11:36:49Z)

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.