1. Download 3D-FRONT dataset from https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset
├──RAW_DATA_PATH
├── 3D-FRONT
├── 3D-FUTURE-model
├── categories.py
├── model_info.json
Note: 3D-FRONT is updated after the paper submission so the preprocessing is a little different from the original paper (marked as DFP below).
cd dataset_3dfront
- Generate
assets/cat2id_all.pkl:
python gen_cat2id_all.py # assets/gen_cat2id_all.py
- Generate
assets/cat2id_bedroom.pklandassets/cat2id_living.pkl
type=bedroom; # or living
python json2layout_distribution.py --type $type
- Generate the split dataset:
type=bedroom; # or living
python json2layout.py --type $type --future_path RAW_DATA_PATH/3D-FUTURE-model --json_path RAW_DATA_PATH/3D-FRONT --model_info_path RAW_DATA_PATH/model_info.json
For bedroom, we merge Bedroom.npy, MasterBedroom.npy, SecondBedroom.npy together. We filter out bedroom without bed. 4111 scenes in total. we use all for training (4000) and validation (111).
python mergeDataset_bedroom.py
For livingroom, we merge LivingRoom.npy, LivingDiningRoom.npy together. 4684 scenes in total. We use all for training (4000) and validation (684, DFP). We filter out scenes with less than 6 objects (DFP 4 in the paper).
python mergeDataset_living.py
Bedroom_train_val.npy and Livingroom_train_val.npy are generated in ./outputs .
type=bedroom; # or living
python gen_3dfront_dataset.py --type $type
The final dataset is stored in ./data/bedroom or ./data/living .
Final dataset:
├──data
├── bedroom
├── 0_abs.npy
├── 0_rel.pkl
├── ...
├── living
├── 0_abs.npy
├── 0_rel.pkl
├── ...
├── train_bedroom.txt
├── train_living.txt
├── val_bedroom.txt
└── val_living.txt