Introduction

3D layout estimation of general rooms based on ordinal semantic segmentation.

Overall architecture

3D reconstruction of room layout

Installation

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

Data preparation

You can download the InteriorNet-layout dataset here: https://github.com/Hui-Yao/InteriorNet-Layout/blob/main/README.md

Training

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

Pre-trained model

You can download our pre-trained model here: https://drive.google.com/drive/folders/1bR4FFUFm7_eUEyav2fu8PUPlJP8i-gf3

Evaluation

Run the following command to evaluate the performance:

python evaluate.py --data_path path_to_testing_set --pretrained_path path_to_predtrained_model

Prediction

Run the following command to predict on a single image:

python predict.py --image_path path_to_image --pretrained_path path_to_predtrained_model