Comments (6)
Hi @erzhu222 . Thank you for your following!
- To my understand, you want to know about the "pre-trained model" loaded before training. The pre-trained model uses the imagenet pre-trained model, as other works did. The models we provide in README.md (res50 and resnext101) are used for inference only, which have been trained following our paper.
- Thanks for reminding me. The DIML dataset can be downloaded in the DiverseDepth project, which can be used directly. We can not find the 3D Ken Burns dataset, but it is not so important during training.
- You can generate the optical flow between stereo images, and take the horizontal optical flow as the disparity value after filtering out the large portrait optical flow(e.g. bigger than 2 pixels). Note that the generated disparity value is up to scale and may contain noise, so it belongs to the low-quality dataset, and only the ranking loss is used during training.
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Thanks for your reply!
- For the first question, I mean the weights you provided (not the weights of backbone), what dataset are they(resnet50 and resnext101) trained on ?
- I see the DIML dataset in the DiverseDepth project, thanks for your sharing.
- I have calculated optical flow with Flownet2 and get the horizontal optical flow, how can I filter the large portrait optical flow(e.g. bigger than 2 pixels)
thanks again!
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For Q1, we trained the models on Taskonomy(part of it), DIML, 3D Ken Burns, Holopix50K and HRWSI, which have been released except for the 3D Ken Burns.
For Q3, you can generate a valid_mask with portrait optical flow smaller than 2 pixels. Then, set the disparity value of invalid mask (~valid_mask) to 0, during which "0" stands for invalid values or regions.
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Thanks,for Q1, DiverseDepth was not used for train?
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I double-checked the paper, and DiverseDepth is not employed for training. But more datasets can bring more accuracy and robustness. Just train it with more data as much as possible.
By the way, if you would like to train on large diverse datasets, you may be interested in our BoostingDepth, whose code and data will be released after accepted.
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I double-checked the paper, and DiverseDepth is not employed for training. But more datasets can bring more accuracy and robustness. Just train it with more data as much as possible.
By the way, if you would like to train on large diverse datasets, you may be interested in our BoostingDepth, whose code and data will be released after accepted.
OK,thanks for your confirmation,I have downloaded the 3D Ken Burns dataset, could you please provide the annotation file when you traind. Thanks again!
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Related Issues (20)
- No module named 'lib' error in AdelaiDepth_ResNet101.ipynb HOT 2
- Where to find "SparseTensor"? HOT 1
- Infinit focal length!!! HOT 1
- Error occurred in LeReS HOT 1
- Error on 'Download' step of Google Collab HOT 3
- how to generate depth data like you provide? HOT 1
- About Taskonomy dataset camera camera intrinsics HOT 2
- Need Pretrained Parameters HOT 1
- Torchsparse v1.2.0 vs. v2.1.0 HOT 1
- Visualize with texture. HOT 1
- DIML dataset missing 'disp' data
- some dataset
- Questions about the training resolution and auxiliary network.
- Evaluation samples on ScanNet
- Docker for RTX class GPU for LeReS
- Problem in downloading Weights HOT 3
- Reconstructing the 3D point cloud when the intrinsic is known
- problem with TorchSparse version HOT 1
- How to get instance planes HOT 1
- Empty .pth of Google Colab HOT 2
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