xraubert2 / nerfstudio_SuGaR

SuGaR implementation to NeRFStudio
https://docs.nerf.studio
Apache License 2.0
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NerfStudio + SuGaR - Initital tests #2

Open xraubert2 opened 2 months ago

xraubert2 commented 2 months ago

Bureau_lightroom_nerfstudio_format_hloc

DATA FOLDER

├── dataset
│   ├── colmap (for NerfStudio)
│   │   ├── sparse
│   │   │   ├── 0
│   │   │   │   ├── camera.bin etc
│   ├── distorted (coming from SuGaR)
│   ├── images (for both)
│   ├── sparse (for Sugar)
│   ├── transforms.json (for Nerfstudio)

source_path='/root/nerfstudio_SuGaR/data/Bureau_lightroom_nerfstudio_format_hloc'


OUTPUT FOLDER

├── 2024-XX-XX_XXXXXX
│   ├── nerfstudio_models (for NerfStudio)
│   ├── point_cloud (coming from ns-export gaussian-splatting)
│   │   ├── iteration_7000
│   │   │   ├── point_cloud.ply
│   ├── cfg_args (for Sugar)
│   ├── cameras.json (for Sugar)
│   ├── input.ply (for Sugar)

model_path='/root/nerfstudio_SuGaR/outputs/Bureau_lightroom_nerfstudio_format_hloc/splatfacto/2024-02-21_121102'


cfg_args file -> contains simple info on the model

Namespace(sh_degree=3, source_path='/root/nerfstudio_SuGaR/data/Bureau_lightroom_nerfstudio_format_hloc', model_path='outputs_GS/Cloitre_Gal2_E2_Statues_proche_bis', images='images', resolution=-1, white_background=False, data_device='cuda', eval=False)


cameras.json -> needs to be converted from the Nerfstudio format to the Sugar format


python train.py -s /root/nerfstudio_SuGaR/data/Bureau_lightroom_nerfstudio_format_hloc/ -c /root/nerfstudio_SuGaR/outputs/Bureau_lightroom_nerfstudio_format_hloc/splatfacto/2024-02-21_121102/ -r "sdf" --low_poly True --poisson_depth 8 --experiment_name Bureau_lightroom_nerfstudio_format_hloc/

the code turns but we get the error:

WARNING: No gaussians available for sampling.                                               
coarse_sdf.py:708
xraubert2 commented 2 months ago

Checking conventions

image image

Bureau Sugar/GS

image

Bureau Splatfacto

image

1, 1, 1

image
xraubert2 commented 2 months ago

1, 1, -1

image
xraubert2 commented 2 months ago

1, -1, 1

image
xraubert2 commented 2 months ago

1, -1, -1

image

-1, -1, -1

image

-1, -1, 1

image
xraubert2 commented 2 months ago

Scaling Splatfacto pcd to GS/Sugar pcd convention:

image

after reloading the .ply with more info of header image

tranformation_matrix [17:24:42] -0.844333350658 0.535432219505 -0.020336233079 10.036691665649 0.087618708611 0.100525870919 -0.991068899632 7.630033969879 -0.528605878353 -0.838574349880 -0.131791219115 -7.633952617645 0.000000000000 0.000000000000 0.000000000000 1.000000000000

second trial with all header of .ply [09:46:44] -0.839185059071 0.543805301189 0.006650578231 10.154912948608 0.062881372869 0.109169028699 -0.992032289505 7.957352161407 -0.540198445320 -0.832080483437 -0.125808238983 -7.050346851349 0.000000000000 0.000000000000 0.000000000000 1.000000000000

Running Sugar on Splatfacto outputs

Test 1: with cameras.json of Sugar

python train.py -s /root/nerfstudio_SuGaR/data/Bureau_lightroom_nerfstudio_format_hloc/ -c /root/nerfstudio_SuGaR/outputs/Bureau_lightroom_nerfstudio_format_hloc/splatfacto/test_Bureau_splatfacto/ -r "sdf" --low_poly True --poisson_depth 8 --experiment_name Bureau_lightroom_nerfstudio_format_hloc/

python train_refined.py -s /root/nerfstudio_SuGaR/data/Bureau_lightroom_nerfstudio_format_hloc/ -c /root/nerfstudio_SuGaR/outputs/Bureau_lightroom_nerfstudio_format_hloc/splatfacto/test_Bureau_splatfacto/ -i 7000 -m output/coarse_mesh/Bureau_lightroom_nerfstudio_format_hloc/sugarmesh_3Dgs7000_sdfestim05_sdfnorm02_level03_decim200000.ply -o output/refined/Bureau_lightroom_nerfstudio_format_hloc/ -v 200000 --experiment_name Bureau_lightroom_nerfstudio_format_hloc/

python extract_refined_mesh_with_texture.py -s /root/nerfstudio_SuGaR/data/Bureau_lightroom_nerfstudio_format_hloc/ -c /root/nerfstudio_SuGaR/outputs/Bureau_lightroom_nerfstudio_format_hloc/splatfacto/test_Bureau_splatfacto/ -i 7000 -m output/refined/Bureau_lightroom_nerfstudio_format_hloc/sugarfine_3Dgs7000_sdfestim05_sdfnorm02_level03_decim200000_normalconsistency01_gaussperface1/15000.pt -o output/refined_mesh/Bureau_lightroom_nerfstudio_format_hloc --experiment_name Bureau_lightroom_nerfstudio_format_hloc/

Test 2: with transform_sugar_convention.json

python train.py \ -s /root/nerfstudio_SuGaR/data/Bureau_lightroom_nerfstudio_format_hloc_Nerfjson_Sugarconv/ \ -c /root/nerfstudio_SuGaR/outputs/Bureau_lightroom_nerfstudio_format_hloc/splatfacto/test_Bureau_splatfacto/ \ -r "sdf" \ --low_poly True \ --poisson_depth 8 \ --experiment_name Bureau_lightroom_nerfstudio_format_hloc_Nerfjson_Sugarconv/

-c outputs_GS/Bureau/ -i 7000 -m output/coarse/Bureau_sdf_low_poly/sugarcoarse_3Dgs7000_sdfestim02_sdfnorm02/15000.pt -l 0.3 -d 200000 -o output/coarse_mesh