Open jyj0121 opened 1 year ago
Hi. Thanks for the appreciation. If it could work well on your dataset with 300 frames, you may want to change the time_grid_init and time_grid_final to work with 32 frames. When there are only 32 time frames, while the time_grid is bigger, then there would be many time frames that cannot get access to learning signals. You can try to set time_grid_init = 8 (0.25 * frame numbers) and time_grid_final = 31 or 15. If it works, feel free to tune it. Let me know if it works or not.
Hello,
I wanted to say thank you for your input on this issue. The comments and responses here have been extremely useful in helping me to successfully train a 100-frame video. I have made modifications to some parts of the neural_3D_dataset_NDC.py code (such as tensor sizes, timestamps, etc.) and tweaked the 'time_grid' parameter in the config file as suggested in the comments. It's now working perfectly, and I appreciate the information shared here.
On a related note, I have another question. Currently, the hexplane model renders at the same frame count as the training data. I was wondering if it would be possible to adjust this to render at an arbitrary frame count of my choosing, and if so, where I would need to make such modifications?
Thank you for sharing your work :)
Hi. Yes you can render at arbitrary frame count as you choice. You just need to have a set of timesteps corresponding to these frame count (ranging from -1 to 1) and pass them to hexplanes.
Hi! Will you relese your trained models in Plenoptic dataset? Thanks!
Hello, I'm really appreciated for your nice work and also sharing your valuable code. I'm trying to train HexPlane model with about 32 frames' multi-view video (like nv3d dataset), but while training I only got a black blank image corresponding to the validation view. (It works fine with the nv3d dataset and my dataset with 300 frames). Should I control some parameters(maybe time_grid_init/time_grid_final? ) according to the number of input frames? Thank you very much.