MonoDepth-PyTorch
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OniroAI
Unofficial implementation of Unsupervised Monocular Depth Estimation neural network MonoDepth in PyTorch
I want to know the loss value of the provided trained model. the provided trained model's parameter: model: ‘resnet18_md’ epochs: 200, learning_rate: 1e-4, batch_size: 8, adjust_lr: True, do_augmentation: True Thank...
Hello, I wonder how to transfer the sparse gt depth map to a dense one like that in your paper. Thanks!!!!
Hello, I trying to test your project that seems very nice, but I can't find how to run a simple test, I tried that: `main_monodepth_pytorch.py --data_dir=test --model_path=models/monodepth_resnet18_001.pth --output_directory=output --input_height=375 --input_width=1242...
Hi, months ago I opened an issue about performance reproducing and I forgot to give answers and feedbacks. Really sorry for that. Now as the previous issue has been closed,...
Thanks for the good job, I want to kown that when only have a single image, whether to put this picture in both image_02 and image_03 ?
Very thank you for your good job. I have studied for several days. I think the output of this code is only the disparities and dispariteis with post-processing. Am I...
hi, thanks for your impressive work, which helps me a lot. but I met the following problem and I have no idea about it. Can you give me some points...
I transform this code into tensorflow 2.0 version and trained a driving dataset, then I try your original code but this question still exist. can you help me? pleeeeeeeeeeeease........
Your code is as follows: x_shifts = disp[:, 0, :, :] # Disparity is passed in NCHW format with 1 channel flow_field = torch.stack((x_base + x_shifts, y_base), dim=3) In grid_sample...
# L-R Consistency right_left_disp = [self.generate_image_left(disp_right_est[i], disp_left_est[i]) for i in range(self.n)] left_right_disp = [self.generate_image_right(disp_left_est[i], disp_right_est[i]) for i in range(self.n)] # L-R Consistency lr_left_loss = [torch.mean(torch.abs(right_left_disp[i] - disp_left_est[i])) for i in...