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google / dynibar

Implementation of DynIBaR Neural Dynamic Image-Based Rendering (CVPR 2023)
https://dynibar.github.io/
Apache License 2.0
846 stars 42 forks source link

Evaluation metrics: LPIPS,SSIM,PSNR #16

Open xiaoyudanaa opened 1 year ago

xiaoyudanaa commented 1 year ago

Many congratulations on such an excellent article, I had some problems reproducing the results. I have experimented with the training weights you posted on the jumping dataset, but the final result is very poor as shown in the figure below, I would like to ask what causes this? I look forward to your reply. (The original set chunk size=8192, but the GPU could not run due to out-of-memory problem, so I set the chunk size to the same 1024 as NSFF) image

zhengqili commented 1 year ago

Did you correctly load the model? This is usually due to the fact that you point to the incorrect model path.

xiaoyudanaa commented 1 year ago

Based on your response, I checked my configuration path and its doesn't seem to be wrong. The details are shown in the attached image： image image

zhengqili commented 1 year ago

When you run the script, from the terminal did you see something like "Reloading from xxxx, starting at step=yyyy" or "No ckpts found, training from scratch..."?

adamInThe80s commented 1 year ago

Hi, I'm not the author of the paper. But I ran into a similar issue as you. In ibrnet/model.py line 104: out_folder = os.path.join(args.rootdir, 'checkpoints/fine', args.expname)

The "fine" model checkpoint location is at a hard-coded location relative to the root directory. So if you are correctly pointing to the coarse checkpoint location in the config file, you might still be running to this problem.

quan5609 commented 7 months ago

Hello @xiaoyudanaa, do you have any summary of per-scene results on nvidia dataset?