kaiqiangxiong / cl-mvsnet Goto Github PK

Thank you for your excellent work, but your method requires too much gpu memory. Have you tested whether training with 96g of gpu memory can achieve a similar reconstruction effect?

When re-training, I get 0.339 overall on DTU instead of the reported 0.329 (using default settings, 8 GPUs). The ICC masking implementation here doesn't look quite right to me - imgs_icc is shape [B, V, 3, H, W], so for batch size 1, the for-loop doesn't actually have any iterations. Should it perhaps be changed to:

imgs_icc = data["imgs_aug"] # b v c h w
nviews = imgs_icc.shape[1]
for view_idx in range(1, nviews, 1):   # loops through view dimension instead of batch dimension
    per = min(self.args.p_icc * epoch / 15, self.args.p_icc)
    mask = torch.ones_like(imgs_icc[:, view_idx]) * per
    mask = 1 - mask.bernoulli() 
    imgs_icc[:, view_idx] = imgs_icc[:, view_idx] * mask