ResPlan: A Large-Scale Vector-Graph Dataset of 17,000 Residential Floor Plans
This dataset provides a more realistic and scalable resource for spatial AI research, benefiting researchers and developers in fields like robotics and virtual reality, though it is incremental over prior datasets.
The authors introduced ResPlan, a dataset of 17,000 detailed residential floor plans with architectural and functional annotations, addressing limitations of existing datasets like RPLAN and MSD by offering enhanced visual fidelity and structural diversity. They provided an open-source pipeline for processing and enabled various applications including robotics, generative AI, and simulations.
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.