3D layout estimation of general rooms based on ordinal semantic segmentation.
The code is tested with Ubuntu 18.04, PyTorch v1.6, CUDA 10.1 and cuDNN v7.6.
## create conda env
conda create -n ordinal python=3.6
## activate conda env
conda activate ordinal
## install pytorch
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch
## install dependencies
pip install -r requirements.txt
You can download the InteriorNet-layout dataset here: https://github.com/Hui-Yao/InteriorNet-Layout/blob/main/README.md
Run the following command to train our network:
python main.py --data_path path-to-the-dataset --model_name the-name-of-a-new-training
You can download our pre-trained model here: https://drive.google.com/drive/folders/1bR4FFUFm7_eUEyav2fu8PUPlJP8i-gf3
Run the following command to evaluate the performance:
python evaluate.py --data_path path_to_testing_set --pretrained_path path_to_predtrained_model
Run the following command to predict on a single image:
python predict.py --image_path path_to_image --pretrained_path path_to_predtrained_model