ToughStoneX/Self-Supervised-MVS

On PyTorch Version & Training Time

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II-Matto commented

Thanks for this great work! I have two quick questions:

  1. Will PyTorch 1.4.0 be OK for running this code? I notice that the recommended version is 1.1.0.
  2. How long will it take to train JDACS (w/o MS)? I notice that the README says training JDACS-MS can take several days with 4 GPUs. Is training JDACS less time-consuming?

BTW, is there any Python implementation of the evaluation code, which is currently implemented with Matlab?

Many thanks.

ToughStoneX commented

Hello,

  1. It is OK to run the code with PyTorch version over 1.1.0. The 1.1.0 is recommended according to the environment of my server.
  2. I remember that JDACS trained with 4 GPUs requires half a day on my server. Whereas JDACS-MS requires several days on 4 GPUs. It is because the backbones of JDACS and JDACS-MS are different. MVSNet is utilized in JDACS and CVP-MVSNet is used in JDACS-MS. The training time is related to the backbone.
  3. For evaluation, you can directly run the test.sh in JDACS-MS and the eval_dense.sh in JDACS. These scripts will generate the 3D models in a format of .ply. The provided Matlab code is from the DTU benchmark, which is used to assess the performance following their official benchmark.