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HLinChen / VCR-GauS

[NeurIPS 2024] VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction
https://hlinchen.github.io/projects/VCR-GauS/
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Strange result on mip-nerf360 dataset #3

Closed scy5335 closed 1 week ago

scy5335 commented 1 week ago

I run "python python_scripts/run_mipnerf360.py" on scene "garden" in mip-nerf360 dataset. And the color of gaussians looks strange and PSNR is about 6-7. image

HLinChen commented 1 week ago

I trained the scene with the code just now. And the PSNR is about 26. This is my rendered color. Maybe there's something wrong with the preprocessing of your data. image

scy5335 commented 1 week ago

I try processed Tanks and Temples dataset and I still get bad result. Will it cause by different environments? I run the code on RTX3090 and the following is pip package addict 2.4.0 args 0.1.0 asttokens 2.4.1 attrs 24.2.0 blinker 1.8.2 certifi 2024.8.30 charset-normalizer 3.4.0 click 8.1.7 clint 0.5.1 cmake 3.30.4 comm 0.2.2 ConfigArgParse 1.7 contourpy 1.3.0 coverage 7.6.2 cycler 0.12.1 dash 2.18.1 dash-core-components 2.0.0 dash-html-components 2.0.0 dash-table 5.0.0 decorator 5.1.1 diff_gaussian_rasterization 0.0.0 docker-pycreds 0.4.0 exceptiongroup 1.2.2 executing 2.1.0 fastjsonschema 2.20.0 filelock 3.16.1 Flask 3.0.3 fonttools 4.54.1 gitdb 4.0.11 GitPython 3.1.43 GPUtil 1.4.0 idna 3.10 imageio 2.35.1 importlib_metadata 8.5.0 iopath 0.1.10 ipython 8.28.0 ipywidgets 8.1.5 itsdangerous 2.2.0 jedi 0.19.1 Jinja2 3.1.4 joblib 1.4.2 jsonschema 4.23.0 jsonschema-specifications 2024.10.1 jupyter_core 5.7.2 jupyterlab_widgets 3.0.13 kiwisolver 1.4.7 lazy_loader 0.4 lightning-utilities 0.11.7 lit 18.1.8 lpips 0.1.4 mamba 0.11.3 MarkupSafe 3.0.1 matplotlib 3.9.2 matplotlib-inline 0.1.7 mediapy 1.2.2 mpmath 1.3.0 nbformat 5.10.4 nest-asyncio 1.6.0 networkx 3.3 ninja 1.11.1.1 numpy 1.26.1 nvidia-cublas-cu11 11.10.3.66 nvidia-cuda-cupti-cu11 11.7.101 nvidia-cuda-nvrtc-cu11 11.7.99 nvidia-cuda-runtime-cu11 11.7.99 nvidia-cudnn-cu11 8.5.0.96 nvidia-cufft-cu11 10.9.0.58 nvidia-curand-cu11 10.2.10.91 nvidia-cusolver-cu11 11.4.0.1 nvidia-cusparse-cu11 11.7.4.91 nvidia-nccl-cu11 2.14.3 nvidia-nvtx-cu11 11.7.91 open3d 0.18.0 opencv-python 4.10.0.84 packaging 24.1 pandas 2.2.3 parso 0.8.4 pexpect 4.9.0 pillow 10.4.0 pip 24.2 platformdirs 4.3.6 plotly 5.24.1 plyfile 1.1 portalocker 2.10.1 prompt_toolkit 3.0.48 protobuf 5.28.2 psutil 6.0.0 ptyprocess 0.7.0 pure_eval 0.2.3 Pygments 2.18.0 pymeshlab 2023.12.post2 pyparsing 3.1.4 pyquaternion 0.9.9 python-dateutil 2.9.0.post0 pytorch3d 0.7.8 pytz 2024.2 PyYAML 6.0.2 referencing 0.35.1 requests 2.32.3 retrying 1.3.4 rpds-py 0.20.0 scikit-image 0.24.0 scikit-learn 1.5.2 scipy 1.14.1 sentry-sdk 2.16.0 setproctitle 1.3.3 setuptools 75.1.0 simple_knn 0.0.0 six 1.16.0 smmap 5.0.1 stack-data 0.6.3 sympy 1.13.3 tenacity 9.0.0 termcolor 2.5.0 threadpoolctl 3.5.0 tifffile 2024.9.20 torch 2.0.1+cu118 torchaudio 2.0.2+cu118 torchmetrics 1.4.3 torchvision 0.15.2+cu118 tqdm 4.66.5 traitlets 5.14.3 trimesh 4.4.9 triton 2.0.0 typing_extensions 4.12.2 tzdata 2024.2 urllib3 2.2.3 vcr-gaus 0.0.0.dev0 /media/common/disk3/scy/surface_reconstruction/VCR-GauS wandb 0.18.3 wcwidth 0.2.13 Werkzeug 3.0.4 wheel 0.44.0 widgetsnbextension 4.0.13 zipp 3.20.2

HLinChen commented 1 week ago

Can you check the initial point clouds created from colmap? Occasionally, the point cloud generated by the colmap can be a bit problematic.

scy5335 commented 1 week ago

There may be something wrong caused by pip. I use "cd submodules/diff-gaussian-rasterization" & "pip install -e ." to reinstall diff-gaussian-rasterization module and the bug is fixed. Thanks for your reply sincerely.