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Despite that significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in the 3D space. Towards reconstructing the room layout in 3D, we formulate the task of 360˚ layout estimation as a problem of predicting depth on the horizon line of a panorama. Specifically, we propose the Differentiable Depth Rendering procedure to make the conversion from layout to depth prediction differentiable, thus making our proposed model end-to-end trainable while leveraging the 3D geometric information, without the need of providing the ground truth depth. We show that our method achieves state-of-the-art performance on numerous 360˚ layout benchmark datasets. Moreover, our formulation enables a pre-training step on the depth dataset, which further improves the generalizability of our layout estimation model.

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CVPR 2021 Oral

LED2-Net: Monocular 360˚ Layout Estimation via Differentiable Depth

Fu-En Wang*, Yu-Hsuan Yeh*, Min Sun, Wei-Chen Chiu, Yi-Hsuan Tsai


[Paper (arXiv)] [Supplementary Marterial] [Code]


      title={LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering}, 
      author={Fu-En Wang and Yu-Hsuan Yeh and Min Sun and Wei-Chen Chiu and Yi-Hsuan Tsai},