Skip to content
Snippets Groups Projects
Commit 6572ce19 authored by Maki94's avatar Maki94
Browse files

scripts to run blender and dtu

parent 603cb081
No related merge requests found
Hide whitespace changes
Inline Side-by-side
arguments/__init__.py
+ 0
− 1
View file @ 6572ce19
......@@ -114,7 +114,6 @@ class ModelHiddenParams(ParamGroup):
self.color_model = 'linear' # mlp, hexplane, 'sh'
self.opacity_model = 'nerf' # nerf or volsdf
self.opacity_ones = False
self.use_deform_net = False
self.opt_pts = False
self.opt_pts_per_frame = False
self.encoder_query_scale = 1.0
......
run_blender.sh 0 → 100644
+ 32
− 0
View file @ 6572ce19
# Example script to run 3DGS, 3DGS with Moran loss, and SplatFields3D on the Blender dataset (Table 2 in the main paper and Tab C1 and C2 in the supplementary material)
set -x
SCENE=lego # any of the Blender scenes
N_VIEWS=10 # in range (4 6 8 10 12)
DATASET_ROOT=/media/STORAGE_4TB/NeRF_datasets/nerf_synthetic
# # to reproduce 3DGS
python train.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/$SCENE/${N_VIEWS}views/3DGS --is_static --n_views $N_VIEWS --iterations 40000 --pts_samples hull --max_num_pts 300000 --load_time_step 0 --composition_rank 0
python render.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/$SCENE/${N_VIEWS}views/3DGS --is_static --n_views $N_VIEWS --iterations 40000 --pts_samples hull --max_num_pts 300000 --load_time_step 0 --composition_rank 0
# to reproduce SplatFields3D
python train.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/${SCENE}/${N_VIEWS}views/SplatFields --encoder_type VarTriPlaneEncoder --D 4 --lambda_norm 0.01 --test_iterations -1 --W 128 --n_views ${N_VIEWS} --iterations 40000 --pts_samples load --max_num_pts 100000 --pc_path ./output_rep/Blender/${SCENE}/${N_VIEWS}views/3DGS/point_cloud/iteration_40000/point_cloud.ply --load_time_step 0 --composition_rank 0
python render.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/${SCENE}/${N_VIEWS}views/SplatFields --encoder_type VarTriPlaneEncoder --D 4 --lambda_norm 0.01 --test_iterations -1 --W 128 --n_views ${N_VIEWS} --iterations 40000 --pts_samples load --max_num_pts 100000 --pc_path ./output_rep/Blender/${SCENE}/${N_VIEWS}views/3DGS/point_cloud/iteration_40000/point_cloud.ply --load_time_step 0 --composition_rank 0
# to reproduce 3DGS w. L_moran
python train.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/$SCENE/${N_VIEWS}views/3DGS_Lmoran --test_iterations -1 --is_static --n_views ${N_VIEWS} --iterations 40000 --pts_samples hull --max_num_pts 300000 --lambda_corr 0.01 --load_time_step 0 --composition_rank 0
python render.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/$SCENE/${N_VIEWS}views/3DGS_Lmoran --test_iterations -1 --is_static --n_views ${N_VIEWS} --iterations 40000 --pts_samples hull --max_num_pts 300000 --lambda_corr 0.01 --load_time_step 0 --composition_rank 0
# to reproduce the ablation study (Table 3 in the main paper)
# 1st row: basic (MLP-only) model:
python train.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/${SCENE}/${N_VIEWS}views/MLP --encoder_type none --test_iterations -1 --W 128 --n_views $N_VIEWS --iterations 40000 --pts_samples load --max_num_pts 300000 --pc_path ./output_rep/Blender/${SCENE}/${N_VIEWS}views/3DGS/point_cloud/iteration_40000/point_cloud.ply --load_time_step 0 --composition_rank 0
python render.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/${SCENE}/${N_VIEWS}views/MLP --encoder_type none --test_iterations -1 --W 128 --n_views $N_VIEWS --iterations 40000 --pts_samples load --max_num_pts 300000 --pc_path ./output_rep/Blender/${SCENE}/${N_VIEWS}views/3DGS/point_cloud/iteration_40000/point_cloud.ply --load_time_step 0 --composition_rank 0
# 2nd row: basic (MLP-only) + L_2 norm:
python train.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/${SCENE}/${N_VIEWS}views/MLP_norm0.01 --lambda_norm 0.01 --encoder_type none --test_iterations -1 --W 128 --n_views $N_VIEWS --iterations 40000 --pts_samples load --max_num_pts 300000 --pc_path ./output_rep/Blender/${SCENE}/${N_VIEWS}views/3DGS/point_cloud/iteration_40000/point_cloud.ply --load_time_step 0 --composition_rank 0
python render.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/${SCENE}/${N_VIEWS}views/MLP_norm0.01 --lambda_norm 0.01 --encoder_type none --test_iterations -1 --W 128 --n_views $N_VIEWS --iterations 40000 --pts_samples load --max_num_pts 300000 --pc_path ./output_rep/Blender/${SCENE}/${N_VIEWS}views/3DGS/point_cloud/iteration_40000/point_cloud.ply --load_time_step 0 --composition_rank 0
# 3rd row: basic (MLP-only) + tri-CNN:
python train.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/${SCENE}/${N_VIEWS}views/MLP_CNN --lambda_norm 0.01 --encoder_type VarTriPlaneEncoder --test_iterations -1 --W 128 --n_views $N_VIEWS --iterations 40000 --pts_samples load --max_num_pts 300000 --pc_path ./output_rep/Blender/${SCENE}/${N_VIEWS}views/3DGS/point_cloud/iteration_40000/point_cloud.ply --load_time_step 0 --composition_rank 0
python render.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/${SCENE}/${N_VIEWS}views/MLP_CNN --lambda_norm 0.01 --encoder_type VarTriPlaneEncoder --test_iterations -1 --W 128 --n_views $N_VIEWS --iterations 40000 --pts_samples load --max_num_pts 300000 --pc_path ./output_rep/Blender/${SCENE}/${N_VIEWS}views/3DGS/point_cloud/iteration_40000/point_cloud.ply --load_time_step 0 --composition_rank 0
# 4th row: full model (MLP+L_2 norm+tri-CNN):
python train.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/${SCENE}/${N_VIEWS}views/MLP_norm0.01_CNN --lambda_norm 0.01 --encoder_type VarTriPlaneEncoder --test_iterations -1 --W 128 --n_views $N_VIEWS --iterations 40000 --pts_samples load --max_num_pts 300000 --pc_path ./output_rep/Blender/${SCENE}/${N_VIEWS}views/3DGS/point_cloud/iteration_40000/point_cloud.ply --load_time_step 0 --composition_rank 0
python render.py -s ${DATASET_ROOT}/${SCENE} --white_background --eval -m ./output_rep/Blender/${SCENE}/${N_VIEWS}views/MLP_norm0.01_CNN --lambda_norm 0.01 --encoder_type VarTriPlaneEncoder --test_iterations -1 --W 128 --n_views $N_VIEWS --iterations 40000 --pts_samples load --max_num_pts 300000 --pc_path ./output_rep/Blender/${SCENE}/${N_VIEWS}views/3DGS/point_cloud/iteration_40000/point_cloud.ply --load_time_step 0 --composition_rank 0
run_dtu.sh
+ 9
− 7
View file @ 6572ce19
# ------------------------------------------------------------------------------
# Example script to run 3DGS and SplatFields3D on the DTU dataset
# RUN vanilla 3DGS on DTU dataset
python train.py -s /media/STORAGE_4TB/NeRF_datasets/DTU/2DGS_data/dtu/DTU/scan110 -m ./output_rep/dtu/scan110/3views/3DGS --white_background --lambda_mask 0.1 -r 2 --is_static --n_views 3 --iterations 30000
python render.py -s /media/STORAGE_4TB/NeRF_datasets/DTU/2DGS_data/dtu/DTU/scan110 -m ./output_rep/dtu/scan110/3views/3DGS --white_background --lambda_mask 0.1 -r 2 --is_static --n_views 3 --iterations 30000
# set the path to the DTU dataset
DATASET_ROOT=/media/STORAGE_4TB/NeRF_datasets/DTU/2DGS_data/dtu/DTU
SCENE=scan114 # scan105 scan106 scan110 scan114 scan118 scan122 scan24 scan37 scan40 scan55 scan63 scan65 scan69 scan83 scan97
N_VIEWS=3
python train.py -s $DATASET_ROOT/$SCENE -m ./output_rep/dtu/$SCENE/3views/3DGS --white_background --lambda_mask 0.1 -r 2 --is_static --n_views $N_VIEWS --iterations 30000
python render.py -s $DATASET_ROOT/$SCENE -m ./output_rep/dtu/$SCENE/3views/3DGS --white_background --lambda_mask 0.1 -r 2 --is_static --n_views $N_VIEWS --iterations 30000
# RUN SplatFields3D on DTU dataset
python train.py -s /media/STORAGE_4TB/NeRF_datasets/DTU/2DGS_data/dtu/DTU/scan110 -m ./output_rep/dtu/scan110/3views/SplatFields3D --pc_path ./output_rep/dtu/scan110/3views/3DGS/point_cloud/iteration_7000/point_cloud.ply --deform_weight 0 --white_background --lambda_mask 0.1 --n_views 3 --lambda_norm 0.01 --encoder_type VarTriPlaneEncoder --W 128 --iterations 30000 --max_num_pts 300000 -r 2 --load_time_step 0 --composition_rank 0
python render.py -s /media/STORAGE_4TB/NeRF_datasets/DTU/2DGS_data/dtu/DTU/scan110 -m ./output_rep/dtu/scan110/3views/SplatFields3D --pc_path ./output_rep/dtu/scan110/3views/3DGS/point_cloud/iteration_7000/point_cloud.ply --deform_weight 0 --white_background --lambda_mask 0.1 --n_views 3 --lambda_norm 0.01 --encoder_type VarTriPlaneEncoder --W 128 --iterations 30000 --max_num_pts 300000 -r 2 --load_time_step 0 --composition_rank 0
# ------------------------------------------------------------------------------
python train.py -s $DATASET_ROOT/$SCENE -m ./output_rep/dtu/$SCENE/3views/SplatFields3D --pc_path ./output_rep/dtu/$SCENE/3views/3DGS/point_cloud/iteration_1000/point_cloud.ply --deform_weight 0 --white_background --lambda_mask 0.1 --n_views $N_VIEWS --lambda_norm 0.01 --encoder_type VarTriPlaneEncoder --W 128 --iterations 30000 --max_num_pts 300000 -r 2 --load_time_step 0 --composition_rank 0
python render.py -s $DATASET_ROOT/$SCENE -m ./output_rep/dtu/$SCENE/3views/SplatFields3D --pc_path ./output_rep/dtu/$SCENE/3views/3DGS/point_cloud/iteration_1000/point_cloud.ply --deform_weight 0 --white_background --lambda_mask 0.1 --n_views $N_VIEWS --lambda_norm 0.01 --encoder_type VarTriPlaneEncoder --W 128 --iterations 30000 --max_num_pts 300000 -r 2 --load_time_step 0 --composition_rank 0
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment