Mu Hu

valid percentage = the number of valid pixels / the number of all pixels (_i.e._ h x w)

Exactly!

Hi! I think you must be referring to Huber loss. L1 and L2 loss are provided in `criteria.py`, and you could use `torch.where ` to conditionally combine them into Huber...

Hi, here is a naive version and you could use it like L1 or L2 losses: ``` class MaskedRuberLoss(nn.Module): def __init__(self, c=1): super(MaskedRuberLoss, self).__init__() self.c = c def forward(self, pred,...

You could add these lines to the criteria.py and create an instance of it in main.py. When initializing, you can define c as you like, and here I just set...

Hi! You could replace the original code in the corresponding function in `utils.py` by the following ones: ``` def depth_colorize(depth, min=1e-3, max=100): depth = (depth - min) / (max -...

I strongly suggest the diverse coloring mapping recipes [here]( https://matplotlib.org/stable/tutorials/colors/colormaps.html) and you could manually set the exception values (extreme large or small). I think setting each interval to a corresponding...

Thanks for your interest! For one specific (rgb, sd, gt) tuple, the supervision is semi-dense and there're many pixels where supervision doesn't exist. However, for the whole dataset, on most...

> > However, for the whole dataset, on most of the pixels in the 2D-image coordinate the network receives supervision from different data samples > > I am so happy...

> Hi, haven't seen you for a while. I'm still interested in this problem, and this Friday, I trained a simple deep completion model using sparse loss functions (paper). But...