ResPlan: A Large-Scale Vector-Graph Dataset of 17,000 Residential Floor Plans

• URL: http://arxiv.org/abs/2508.14006v1
• Date: Tue, 19 Aug 2025 17:07:47 GMT
• Title: ResPlan: A Large-Scale Vector-Graph Dataset of 17,000 Residential Floor Plans
• Abstract summary: We introduce ResPlan, a large-scale dataset of 17,000 detailed, structurally rich, and realistic residential floor plans.<n>Each plan includes precise annotations of architectural elements (walls, doors, windows, balconies) and functional spaces.<n>ResPlan supports a wide range of applications including robotics, reinforcement learning, generative AI, virtual and augmented reality, simulations, and game development.
• Score: 0.9208007322096533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce ResPlan, a large-scale dataset of 17,000 detailed, structurally rich, and realistic residential floor plans, created to advance spatial AI research. Each plan includes precise annotations of architectural elements (walls, doors, windows, balconies) and functional spaces (such as kitchens, bedrooms, and bathrooms). ResPlan addresses key limitations of existing datasets such as RPLAN (Wu et al., 2019) and MSD (van Engelenburg et al., 2024) by offering enhanced visual fidelity and greater structural diversity, reflecting realistic and non-idealized residential layouts. Designed as a versatile, general-purpose resource, ResPlan supports a wide range of applications including robotics, reinforcement learning, generative AI, virtual and augmented reality, simulations, and game development. Plans are provided in both geometric and graph-based formats, enabling direct integration into simulation engines and fast 3D conversion. A key contribution is an open-source pipeline for geometry cleaning, alignment, and annotation refinement. Additionally, ResPlan includes structured representations of room connectivity, supporting graph-based spatial reasoning tasks. Finally, we present comparative analyses with existing benchmarks and outline several open benchmark tasks enabled by ResPlan. Ultimately, ResPlan offers a significant advance in scale, realism, and usability, providing a robust foundation for developing and benchmarking next-generation spatial intelligence systems.

Related papers

• Architect-Ant: Editable Automatic Furnishing of Architectural Floor Plans [73.84806303977797]
  We present Architect-Ant, an automatic furnishing framework powered by a fine-tuned vision-tuned model.<n>Furniture layouts are represented using a compact, coordinate-based domain-specific language (language) that encodes object placements.<n>We show that Architect-Ant produces geometrically valid and functionally plausible layouts, and suggest a scalable path for furnishing larger structure-only floor-plan datasets.
  arXiv  Detail & Related papers  (2026-06-09T14:55:43Z)
• HomeWorld: A Unified Floorplan-to-Furnished Framework for Generating Controllable, Densely Interactive Whole-Home Scenes [36.81011113715663]
  Indoor scene generation is crucial for robot simulation and modern interior design.<n>We propose a unified hierarchical framework that decomposes indoor scene synthesis into controllable stages.<n> Experiments and user studies demonstrate that our pipeline produces indoor spaces with greater layout diversity and stronger 3D design appeal.
  arXiv  Detail & Related papers  (2026-06-04T16:58:43Z)
• LiAuto-GeoX: Efficient Grounded Driving Transformer [54.23823436153608]
  We present textbfLiAuto-GeoX, an efficient grounded driving transformer for deployable, ego-centric 3D scene understanding.<n>We show that textbfLiAuto-GeoX runs at 220 FPS on KITTI while maintaining high-fidelity dense reconstruction, enabling real-time deployment.
  arXiv  Detail & Related papers  (2026-06-04T06:58:44Z)
• TodoEvolve: Learning to Architect Agent Planning Systems [68.48983335970901]
  TodoEvolve is a meta-planning paradigm that autonomously synthesizes and dynamically revises task-specific planning.<n>PlanFactory provides a common interface for heterogeneous planning patterns.<n>TodoEvolve consistently surpasses carefully engineered planning modules while maintaining economical API costs and runtime overhead.
  arXiv  Detail & Related papers  (2026-02-08T06:37:01Z)
• ObjSplat: Geometry-Aware Gaussian Surfels for Active Object Reconstruction [2.8012387812933035]
  Splat is an active reconstruction framework that reconstructs objects with both appearance and accurate geometry.<n>Splat produces physically consistent completeness within minutes, achieving superior reconstruction fidelity and surface completeness compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-01-11T17:14:33Z)
• PLANA3R: Zero-shot Metric Planar 3D Reconstruction via Feed-Forward Planar Splatting [56.188624157291024]
  We introduce PLANA3R, a pose-free framework for metric Planar 3D Reconstruction from unposed two-view images.<n>Unlike prior feedforward methods that require 3D plane annotations during training, PLANA3R learns planar 3D structures without explicit plane supervision.<n>We validate PLANA3R on multiple indoor-scene datasets with metric supervision and demonstrate strong generalization to out-of-domain indoor environments.
  arXiv  Detail & Related papers  (2025-10-21T15:15:33Z)
• GSPlane: Concise and Accurate Planar Reconstruction via Structured Representation [30.083162532688096]
  We propose GSPlane, which recovers accurate geometry and produces clean and well-structured mesh connectivity for plane regions.<n>We also explore applications of the structured planar representation, which enable decoupling and flexible manipulation of objects on supportive planes.
  arXiv  Detail & Related papers  (2025-10-20T01:59:21Z)
• R2RGEN: Real-to-Real 3D Data Generation for Spatially Generalized Manipulation [74.41728218960465]
  We propose a real-to-real 3D data generation framework (R2RGen) that directly augments the pointcloud observation-action pairs to generate real-world data.<n>R2RGen substantially enhances data efficiency on extensive experiments and demonstrates strong potential for scaling and application on mobile manipulation.
  arXiv  Detail & Related papers  (2025-10-09T17:55:44Z)
• Transformer Based Building Boundary Reconstruction using Attraction Field Maps [0.3749861135832072]
  This paper introduces a novel deep learning methodology leveraging Graph Convolutional Networks (GCNs) to address these challenges in building footprint reconstruction.<n>Our model, Decoupled-PolyGCN, outperforms existing methods by 6% in AP and 10% in AR, demonstrating its ability to deliver accurate and regularized building footprints.
  arXiv  Detail & Related papers  (2025-07-22T21:53:03Z)
• E3D-Bench: A Benchmark for End-to-End 3D Geometric Foundation Models [78.1674905950243]
  We present the first comprehensive benchmark for 3D geometric foundation models (GFMs)<n>GFMs directly predict dense 3D representations in a single feed-forward pass, eliminating the need for slow or unavailable precomputed camera parameters.<n>We evaluate 16 state-of-the-art GFMs, revealing their strengths and limitations across tasks and domains.<n>All code, evaluation scripts, and processed data will be publicly released to accelerate research in 3D spatial intelligence.
  arXiv  Detail & Related papers  (2025-06-02T17:53:09Z)
• MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes [6.9924720592711935]
  We develop textbfModified Swiss Dwellings (MSD) -- the first large-scale floor plan dataset that contains a significant share of layouts of multi-apartment dwellings. MSD features over 5.3K floor plans of medium- to large-scale building complexes, covering over 18.9K distinct apartments.
  arXiv  Detail & Related papers  (2024-07-14T08:51:25Z)
• PlanT: Explainable Planning Transformers via Object-Level Representations [64.93938686101309]
  PlanT is a novel approach for planning in the context of self-driving. PlanT is based on imitation learning with a compact object-level input representation. Our results indicate that PlanT can focus on the most relevant object in the scene, even when this object is geometrically distant.
  arXiv  Detail & Related papers  (2022-10-25T17:59:46Z)
• ReCo: A Dataset for Residential Community Layout Planning [21.5996284220876]
  ReCo is presented in multiple data formats with 37,646 residential community layout plans, covering 598,728 residential buildings with height information. To validate the utility of ReCo in automated residential community layout planning, two Generative Adversarial Network (GAN) based generative models are applied to the dataset.
  arXiv  Detail & Related papers  (2022-06-08T17:19:55Z)
• Habitat 2.0: Training Home Assistants to Rearrange their Habitat [122.54624752876276]
  We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual robots in interactive 3D environments. We make contributions to all levels of the embodied AI stack - data, simulation, and benchmark tasks.
  arXiv  Detail & Related papers  (2021-06-28T05:42:15Z)
• House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation [59.86153321871127]
  The main idea is to encode the constraint into the graph structure of its relational networks. We have demonstrated the proposed architecture for a new house layout generation problem.
  arXiv  Detail & Related papers  (2020-03-16T03:16:12Z)

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.