Open davidpengiupui opened 1 month ago
Here are the commands that I used
cd preprocess
python quantize_features.py --config configs/mipnerf360/garden.cfg
cd ..
python train.py --config configs/mipnerf360/garden.cfg
python render_mask.py --config configs/mipnerf360-rendering/garden.cfg
cp -r data/mipnerf360/garden/segmentations output/mipnerf360/garden/0/open_new_eval_softmax_s10.0_a05/
python eval.py --path output/mipnerf360/garden/0/open_new_eval_softmax_s10.0_a05
Hello, how about the generated masks in output/mipnerf360/garden/0/open_new_eval_softmax_s10.0_a05/? Do they appear normal?
The generated masks look not very good. For example, the wood pot:
and the windows
You can use pdb to trace the source of the invalid value, such as the possibility of gt not being read properly or the count having a denominator of 0. Bad masks would typically lead to a low value rather than NaN.
Thank you so much for your great work! I met some problems when trying to run on the "garden" scene of MipNeRF360 and really need your help. I could get normal loss after training, but when evaluating, the results are almost all Nan.
This is after training
And this is after evaluation
![bad_result_LEGaussian](https://github.com/buaavrcg/LEGaussians/assets/90153025/3078fc5a-0cc2-448e-9abe-222239d1dffe)