Running with custom dataset

[bmikaili]

Hey, do you have pointers on how to run this with a custom dataset?

[Kevin-2017]

The NeRF Guru provides an excellent tutorial on this topic, which you can find at this link: To summarize the process: 1)Extract Frames from Video: Use the command FFMPEG -i {path to video} -qscale:v 1 -qmin 1 -vf fps={frame extraction rate} %04d.jpg to sample images from your video. 2)Obtain Camera Poses: Utilize convert.py to get camera poses. Note that this step requires having COLMAP installed. 3)Start Training :D For additional details, refer to the "Processing your own Scenes" section in the Gaussian-Splatting repository.

[bmikaili]

Ah I see, so it's like in the original paper.

but how would I train with pruning? Like with bash scripts/run_train_densify_prune.sh?

[Kevin-2017]

Yeah, you can modify the path and pruning ratio in the sh file. Or you can directly run the Python command with the default setting: python train_densify_prune.py -s "/PATH/TO/DATASET/" -m "OUTPUT/PATH/" BTW, if your primary interest lies in the final result, I suggest modifying the --save_iterations and --checkpoint_iterations arguments to a lower number of iterations. The current defaults are set to save additional data points, primarily for experimental purposes.

[bmikaili]

Thanks for the pointers, I'll try that out! Awesome work btw :)