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Render

Render all

python render.py -m output/<DATASET>/<NAME> --load_mask_on_the_fly --load_image_on_the_fly --eval --load2gpu_on_the_fly --skip_train --iteration 30000 ## configure --multithread_save for faster processing when running on large-RAM machine

Render with text prompt

python render.py -m output/<DATASET>/<NAME> --load_mask_on_the_fly --load_image_on_the_fly --eval --load2gpu_on_the_fly --skip_train --iteration 30000 --text_prompt "TEXT_PROMPT" ## configure --multithread_save for faster processing when running on large-RAM machine

Render with cluster ID

When you click the object in the GUI. You can see which cluster IDs are segemented.

python render.py -m output/<DATASET>/<NAME> --load_mask_on_the_fly --load_image_on_the_fly --eval --load2gpu_on_the_fly --skip_train --iteration 30000 --segment_ids <1> <2> <...> ## configure --multithread_save for faster processing when running on large-RAM machine

## e.g. python render.py -s <path_to_dataset>/HyperNeRF/misc/split-cookie -m ./sadg_example_models/split-cookie --load_mask_on_the_fly --load_image_on_the_fly --eval --load2gpu_on_the_fly --skip_train --iteration 30000 --end_frame -1 --segment_ids 17 --score_threshold 0.9
## e.g. python render.py -s <path_to_dataset>/Neu3D/sear_steak -m ./sadg_example_models/sear_steak --load_mask_on_the_fly --load_image_on_the_fly --eval --load2gpu_on_the_fly --skip_train --iteration 30000 --end_frame -1 --segment_ids 8 --score_threshold 0.98