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

TL;DR
ResPlan is a comprehensive, large-scale dataset of 17,000 realistic residential floor plans with detailed annotations, designed to advance spatial AI research and applications across multiple domains.
Contribution
It introduces ResPlan, a novel dataset with enhanced realism, structural diversity, and versatile formats, along with an open-source pipeline for annotation and analysis, surpassing existing datasets.
Findings
ResPlan enables new benchmark tasks in spatial reasoning.
The dataset improves the realism and diversity of residential layouts.
Open-source tools facilitate easy integration and annotation refinement.
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…
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