Monocular 360˚ Layout Estimation via Differentiable Depth Rendering
LED2-Net
This is PyTorch implementation of our CVPR 2021 Oral paper “LED2-Net: Monocular 360˚ Layout Estimation via Differentiable Depth Rendering”.
You can visit our project website and upload your own panorama to see the 3D results!
Prerequisite
This repo is primarily based on PyTorch. You can use the follwoing command to intall the dependencies.
pip install -r requirements.txt
Preparing Training Data
Under LED2Net/Dataset, we provide the dataloader of Matterport3D and Realtor360. The annotation formats of the two datasets follows PanoAnnotator. The detailed description of the format is explained in LayoutMP3D.
Under config/, config_mp3d.yaml and config_realtor360.yaml are the configuration file for Matterport3D and Realtor360.
Matterport3D
To train/val on Matterport3D, please modify the two items in config_mp3d.yaml.