# BuildingBRep-11K: Precise Multi-Storey B-Rep Building Solids with Rich Layout Metadata

arXiv: https://arxiv.org/abs/2506.15718 · HTML: https://mlsanity.com/paper/2506.15718/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching](https://mlsanity.com/paper/2311.11284/) (2023)
- [Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset](https://mlsanity.com/paper/2107.04286/) (2021)
- [HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising](https://mlsanity.com/paper/2211.13287/) (2022)
- [CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis](https://mlsanity.com/paper/1904.01920/) (2019)
- [BuildingNet: Learning to Label 3D Buildings](https://mlsanity.com/paper/2110.04955/) (2021)
- [Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds](https://mlsanity.com/paper/2307.11914/) (2023)
- [City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds](https://mlsanity.com/paper/2201.10276/) (2022)
- [MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes](https://mlsanity.com/paper/2407.10121/) (2024)
- [ActFloor-GAN: Activity-Guided Adversarial Networks for Human-Centric Floorplan Design](https://mlsanity.com/paper/2111.03545/) (2021)
- [AutoEncoding Tree for City Generation and Applications](https://mlsanity.com/paper/2309.15941/) (2023)

## Abstract (arXiv)

With the rise of artificial intelligence, the automatic generation of building-scale 3-D objects has become an active research topic, yet training such models still demands large, clean and richly annotated datasets. We introduce BuildingBRep-11K, a collection of 11 978 multi-storey (2-10 floors) buildings (about 10 GB) produced by a shape-grammar-driven pipeline that encodes established building-design principles. Every sample consists of a geometrically exact B-rep solid-covering floors, walls, slabs and rule-based openings-together with a fast-loading .npy metadata file that records detailed per-floor parameters. The generator incorporates constraints on spatial scale, daylight optimisation and interior layout, and the resulting objects pass multi-stage filters that remove Boolean failures, undersized rooms and extreme aspect ratios, ensuring compliance with architectural standards. To verify the dataset's learnability we trained two lightweight PointNet baselines. (i) Multi-attribute regression. A single encoder predicts storey count, total rooms, per-storey vector and mean room area from a 4 000-point cloud. On 100 unseen buildings it attains 0.37-storey MAE (87 % within ±1), 5.7-room MAE, and 3.2 m2 MAE on mean area. (ii) Defect detection. With the same backbone we classify GOOD versus DEFECT; on a balanced 100-model set the network reaches 54 % accuracy, recalling 82 % of true defects at 53 % precision (41 TP, 9 FN, 37 FP, 13 TN). These pilots show that BuildingBRep-11K is learnable yet non-trivial for both geometric regression and topological quality assessment