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. […]
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