It seems that the 6dof camera poses of panoramas are required to do the projection. Hence, precisely speaking, the method is not fully self-supervised but requires camera pose ground truth. This is usually accessible, easier compared to the ground truth layout, but may also cause error for the layout projection and thus hurts the overall finetuning performance.
The experiment could be stronger to demonstrate the effectiveness of the method from two aspects: 1) a stronger baseline. It seems SSLayout360 is in general outperforming HorizonNet. It would be convincing to show that this method is able to improve powerful backbones. 2) analyze the domain gap. It would be nice to add some discussions about the gap between datasets. Some datasets are closer to each other thus the adaption may not be a big issue. Also, if the method is able to finetune a pre-trained model on synthetic data, then the value of the approach would be much higher.
2) analyze the domain gap. It would be nice to add some discussions about the gap between datasets. Some datasets are closer to each other thus the adaption may not be a big issue. Also, if the method is able to finetune a pre-trained model on synthetic data, then the value of the approach would be much higher.