Question about scaling factor in evaluation script

I was wondering what is the motivation for using this scaling factor during evaluation?
` depth_pred *= torch.median(depth_gt) / [torch.median(depth_pred)`](url)
https://github.com/facebookresearch/DistDepth/blob/dde30a4cecd5457f3c4fa7ab7bf7d5c8ea95934a/execute_func.py

If we remove this scaling, the results are not aligned with what is reported in the paper. For NYUv2 dataset, the RMSE increases from `0.58` to `0.99`

Can you please explain why did you use that scaling during evaluation? Is it reasonable to scale-up from ground truth information?

Also in [compute_depth_errors()](https://github.com/facebookresearch/DistDepth/blob/0414b6a30f3fc7310d29d4ccb8062d4a597ca3a2/layers.py#:~:text=a1%20%3D%20(thresh%20%3C%201.25,3).float().mean()), how did you decide on `1.25` as a threshold?