process.sh

#!/bin/bash

# get the language feature of the scene
python preprocess.py --dataset_name $dataset_path

# train the autoencoder
cd autoencoder
python train.py --dataset_name $dataset_path --encoder_dims 256 128 64 32 3 --decoder_dims 16 32 64 128 256 256 512 --lr 0.0007 --output ae_ckpt
# get the 3-dims language feature of the scene
python test.py --dataset_name $dataset_path --output

# ATTENTION: Before you train the LangSplat, please follow https://github.com/graphdeco-inria/gaussian-splatting
# to train the RGB 3D Gaussian Splatting model.
# put the path of your RGB model after '--start_checkpoint'

for level in 1 2 3
do
    python train.py -s dataset_path -m output/${casename} --start_checkpoint $dataset_path/output/$casename/chkpnt30000.pth --feature_level ${level}
done

for level in 1 2 3
do
    # render rgb
    python render.py -m output/${casename}_${level}
    # render language features
    python render.py -m output/${casename}_${level} --include_feature
done