NUM_ENVIRONMENTS setting

[zhangpingrui]

This is really a nice work! But there are some questions I want to ask.
I found that when I set NUM_ENVIRONMENTS>1, this code will run fail. Is that because in this method NUM_ENVIRONMENTSonly can be 1? And by the way, how much time does the training procedure take?

[MrZihan]

Hello! Currently our code only allows setting NUM_ENVIRONMENTS to 1. We are working on improving it to support NUM_ENVIRONMENTS > 1. Additionally, we used 4 RTX 3090 GPUs to pre-train the HNR model for over one week. If you find the current code settings to be slow for training, you can try the following methods:

1. In NeRF/models/Policy_ViewSelection_ETP.py, reduce the number of point clouds saved for RGB rendering; this will not affect the quality of the predicted semantic representation.
   

2. In Lookahead\NeRF\models\etp\nerf.py, reduce the number of sampled rays used for RGB rendering training within each batch.
   

3. In Lookahead\NeRF\ss_trainer_ETP.py, reduce the number of subregions used for local semantic alignment.
   

If only novel view representation is needed and RGB rendering is not required, you can remove the code used for RGB rendering. This will result in much faster training.

If you have any other questions, please feel free to ask.

[zhangpingrui]

Hello! Currently our code only allows setting NUM_ENVIRONMENTS to 1. We are working on improving it to support NUM_ENVIRONMENTS > 1. Additionally, we used 4 RTX 3090 GPUs to pre-train the HNR model for over one week. If you find the current code settings to be slow for training, you can try the following methods:

1. In NeRF/models/Policy_ViewSelection_ETP.py, reduce the number of point clouds saved for RGB rendering; this will not affect the quality of the predicted semantic representation.
   
2. In Lookahead\NeRF\models\etp\nerf.py, reduce the number of sampled rays used for RGB rendering training within each batch.
   
3. In Lookahead\NeRF\ss_trainer_ETP.py, reduce the number of subregions used for local semantic alignment.
   

If only novel view representation is needed and RGB rendering is not required, you can remove the code used for RGB rendering. This will result in much faster training.

If you have any other questions, please feel free to ask.

Thanks for your reply! It's really helpful!