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Ctrl-Room dataset introduced in "Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraints"
paper | project page | code
This repository hosts the Ctrl-Room dataset, a collection of semantically annotated 3D scenes derived from the Structured3D dataset. It contains 11,986 filtered rooms, including 4,961 bedrooms, 3,039 living rooms, 1,848 kitchens, 1,500 bathrooms, and 638 studies. Additionally, we also provide the scene descriptions following the format in SceneFormer for each room, which can be used for training text-to-3D generative models.
Dataset Structure
Outlines of the dataset files:
Ctrl-Room-Dataset/
βββ train/ # train split
β βββ bedroom/ # bedroom scenes
| | βββ bbox_3d/ # 3D bounding box annotations (.json files)
| | βββ cam_pos/ # camera positions
| | βββ depth/ # panoramic depth maps
| | βββ img/ # panoramic RGB images
| | βββ label_cor/ # 2D room_layout annotations for each panoramic RGB image
| | βββ quad_walls/ # 3D room_layout annotations (.json files)
| | βββ room_type/ # room types
| | βββ sem_layout_img/ # semantic layout panorama images
| | βββ text_desc/ # text prompt for each room
| | βββ text_desc_emb/ # pre-computed text embeddings using CLIP
β | βββ train_dataset_stats.json # dataset statistic information
β βββ living_room/ # living room scenes
β βββ kitchen/ # kitchen scenes
β βββ study/ # study scenes
β βββ bathroom/ # bathroom scenes
βββ test/ # test split
β βββ bedroom/ # bedroom scenes
β βββ living_room/ # living room scenes
β βββ kitchen/ # kitchen scenes
β βββ study/ # study scenes
β βββ bathroom/ # bathroom scenes
βββ bedroom_splits.json # split file for bedroom
βββ livingroom_splits.json # split file for living room
βββ kitchen_splits.json # split file for kitchen
βββ study_splits.json # split file for study
Citation
If you can make use of it in your own research, please be so kind to cite us.
@article{Ctrl-Room,
title = {Ctrl-room: Controllable text-to-3d room meshes generation with layout constraints},
author = {Chuan Fang, Yuan Dong, Kunming Luo, Xiaotao Hu, Rakesh Shrestha, Ping Tan},
journal = {2025 International Conference on 3D Vision (3DV).},
year = {2025},
eprint = {2025: 692-701.},
archivePrefix = {IEEE},
primaryClass = {cs.CV}
}
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