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Hey, hope you're doing well.
I've been experimenting with your codebase using my own captures, and converting them to the Dynerf structure.
It seems to reconstruct the static scene well (same as standard 3DGS).
However, the dynamic part is often ignored, maybe the movement is too large?
What parameters are the most relevant to mitigate this?
Attached is an example frame: The dynamic part (cyclist in this case) is almost invisible.
I'm guessing time_smoothness_weight should play a part in this, not sure about the rest.
Thanks.
The text was updated successfully, but these errors were encountered:
I've been stuck on (and still am) avoiding overtraining scenes with sparse views. Through this there's a couple things I think I understand better -> Namely that the regularisers, except from the Plane TV, won't have a significant impact on the final result (unless the weights are really small/large). Essentially, if you are not getting "okay" results without the temporal regularisers, then using them/tuning them wont make a big difference.
As @guanjunwu alludes to, plane based methods are a lot more sensitive to the initialisation. So you may want to consider other methods (such as dust3R) to improve point-cloud initialisation, or perhaps increasing the bounding region and number of initial PCs. Additionally, for cases such as mine, lower learning rate and longer training time can help avoid overfitting (even if it takes very long to train).
Hey, hope you're doing well.
I've been experimenting with your codebase using my own captures, and converting them to the Dynerf structure.
It seems to reconstruct the static scene well (same as standard 3DGS).
However, the dynamic part is often ignored, maybe the movement is too large?
What parameters are the most relevant to mitigate this?
Attached is an example frame: The dynamic part (cyclist in this case) is almost invisible.
I'm guessing time_smoothness_weight should play a part in this, not sure about the rest.
Thanks.
The text was updated successfully, but these errors were encountered: