Hi! I use the script "train_blend.sh" and the default setting to fine-tune the released pre-trained model on the BlendedMVS and hope to reproduce the result on Tanks and Temples. I have one NVIDIA 3090 (24G), but I met the out-of-memory problem. Do I need to change some settings like the "--max_h" and "--max_w"?

I success using eval.sh and generate "intermediate" point cloud by fusion.sh. Can you remind me how to submit these data to tank and temple website .**Because I don't have *.log file, so I guess to use ***.log file in TAT databenchmark from mvsnet. I just try to upload data to TAT ,can you help me ?

First, let me add my thanks for your excellent work.

With a single GPU setup, I infer that I should set 'batch=1' in 'env.sh'. Is that correct?

Using the default parameters with batch size of 1, computing depth for DTU models takes ~170 seconds per image on a single GPU (nVidia 1060).

Does that seem correct?

Hi, thanks for your great work! I find the mvsnet_cls_loss useful for improving the representational ability of the classification schedule in MVS network. However, when I transfer mvsnet_cls_loss to other CasMVSNet-like frameworks, I get nan value of loss during training sometimes. Have you meet this problem before? Hoping for your reply~

BTW, does the mvsnet_cls_loss corresponds to finer ground-truth in the paper?

Hello, thank you for your great work and generous contribution. There‘re two details in code: (1) in the script(train_dtu.sh), # make sure num_depth* interval = 203.45. (2) in t&t datasets, the depths are reversed. What's the role of these two operations？

I tried to run eval.sh and get this error, with both DTU and TNT:

RuntimeError: Error(s) in loading state_dict for AARMVSNet: Missing key(s) in state_dict: "feature.init_conv.0.0.weight", "feature.init_conv.0.0.bias",..... Unexpected key(s) in state_dict: "module.feature.init_conv.0.0.weight"...

When I trained, I changed the view_num from 7 to 2 and batch size to 1, because of memory and time (I'm running in colab).

Hello, I tested blended_MVS before, using the model you gave me. The GPU is 3090, but the memory of the GPU is insufficient when executing the test script. How do you set the test parameters and get the results

Hello, Dr. @QT-Zhu Recently, I'm reading your survey paper, "Deep Learning for Multi-view Stereo via Plane Sweep: A Survey". I found the Fig. 2 of the paper is visually appealing and understandable. Now I want to make photos of plane sweeping using my own 3D scene and 2D images, how could I achieve it? Could you give me some suggestions? Looking forward to your reply.

很抱歉打扰到您了，最近使用您的代码实现MVSNet的深度估计时 ，遇到了很难解决的问题，想请教一下。 在获取相机内外参数时使用了colmap（https://github.com/colmap/colmap/tree/3.5）来估计的，然后使用caolmap2mvsnet.py 将colmap估计的结果转换成MVSNet的输入格式。 目前问题是colmap估计的内外参和真值差距很大。下图是输入DTU数据时colmap估计的结果 下图是DTU提供的直 数据差异很大。 并且当使用估计的内外参作为网络输入时，输出的深度估计完全不正确。请问有什么解决方法吗？ 谢谢！

Thanks for your great works!

I am retraining AA-RMVSNet in DTU dataset with default settings on one V100 32GB GPU. But it cost about 17GB for batchsize=1, and batchsize=2 will cause OOM problem.

It is really strange because in the paper, batchsize=4 costs only 20.16GB. Besides, the depth_num is set as 192 in the paper, while it is just 150 in the default setting.

Another question is that the training is very slow. It cost about 4.6s for one step of batch=1.

Can you provide any advice on it?

Hi, thanks for a great work.
Could you provide a script to train/test/eval on custom data? Readme only suggests to prepare data in MVSNet format, but didn't say how to run on it.