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peterzs ashish00510 agitoz rozentill no-materials nnu-gisa kratosaba27 zhangtianer521 chuong vinayak-pathak hcp6897 lizonly montehoover simonpig terrisgo leihui6

deep-geometric-prior's Issues

Could you pls update utils.py?

Hi, I've noticed your utils.py seems to be outdated.
Since I tried to run the reconstruct_single_patch.py and found the module pcu has no methods called read_off/read_ply...
I suppose it's about you having updated pcu module but not utils.py?

Could you share the results under SRB?

Hi, thanks for your work.

I found the 'make_patch' is very time-consuming for the SRB benchmark and for some reason, I do not have enough time to run it....

Could you share the results under SRB?

Greatly appreciate it, I will never forget your favor.

Probably not working with python 3.8+

Hi, following the instructions I get this error in console:

(base) PS C:\Users\G\Desktop\PYTHON_graphs> cd C:\Users\G\repositories\deep-geometric-prior
(base) PS C:\Users\G\repositories\deep-geometric-prior> conda env create -f environment.yml
Collecting package metadata (repodata.json): done
Solving environment: failed

ResolvePackageNotFound:
  - qt==5.6.3=h8bf5577_3
  - libffi==3.2.1=hd88cf55_4
  - libxcb==1.13=h14c3975_1002
  - ptyprocess==0.6.0=py37_0
  - xorg-xproto==7.0.31=h14c3975_1007
  - kiwisolver==1.1.0=py37hc9558a2_0
  - pytorch==1.1.0=py3.7_cuda9.0.176_cudnn7.5.1_0
  - pillow==6.2.1=py37h34e0f95_0
  - libstdcxx-ng==9.1.0=hdf63c60_0
  - sqlite==3.30.1=h7b6447c_0
  - torchvision==0.3.0=py37_cu9.0.176_1
  - vtk==8.2.0=py37ha8e561a_201
  - xorg-libx11==1.6.9=h516909a_0
  - libnetcdf==4.6.2=hbdf4f91_1001
  - openssl==1.1.1d=h516909a_0
  - xorg-libxt==1.2.0=h516909a_0
  - mayavi==4.7.1=py37h7eb8c7e_2
  - libssh2==1.8.2=h22169c7_2
  - xorg-libsm==1.2.3=h84519dc_1000
  - icu==58.2=hf484d3e_1000
  - traits==5.2.0=py37h516909a_0
  - zstd==1.3.7=h0b5b093_0
  - glib==2.58.3=py37h6f030ca_1002
  - zlib==1.2.11=h7b6447c_3
  - tornado==6.0.3=py37h516909a_0
  - numpy==1.17.3=py37hd14ec0e_0
  - numpy-base==1.17.3=py37hde5b4d6_0
  - bzip2==1.0.8=h516909a_1
  - matplotlib==2.2.2=py37hb69df0a_2
  - readline==7.0=h7b6447c_5
  - tk==8.6.8=hbc83047_0
  - mkl_random==1.1.0=py37hd6b4f25_0
  - cudatoolkit==9.0=h13b8566_0
  - mkl==2019.4=243
  - dbus==1.13.6=he372182_0
  - pcre==8.43=he1b5a44_0
  - fontconfig==2.13.1=he4413a7_1000
  - libgfortran-ng==7.3.0=hdf63c60_0
  - pyqt==5.6.0=py37h13b7fb3_1008
  - xz==5.2.4=h14c3975_4
  - gst-plugins-base==1.14.5=h0935bb2_0
  - hdf5==1.10.4=nompi_h3c11f04_1106
  - scipy==1.3.1=py37h7c811a0_0
  - jpeg==9c=h14c3975_1001
  - python==3.7.5=h0371630_0
  - xorg-libice==1.0.10=h516909a_0
  - xorg-libxau==1.0.9=h14c3975_0
  - expat==2.2.5=he1b5a44_1004
  - libtiff==4.1.0=h2733197_0
  - freetype==2.9.1=h8a8886c_1
  - jsoncpp==1.8.4=hc9558a2_1002
  - libpng==1.6.37=hbc83047_0
  - pthread-stubs==0.4=h14c3975_1001
  - sip==4.18.1=py37hf484d3e_1000
  - hdf4==4.2.13=hf30be14_1003
  - lz4-c==1.8.3=he1b5a44_1001
  - libgcc-ng==9.1.0=hdf63c60_0
  - libiconv==1.15=h516909a_1005
  - mkl-service==2.3.0=py37he904b0f_0
  - ncurses==6.1=he6710b0_1
  - ninja==1.9.0=py37hfd86e86_0
  - xorg-kbproto==1.0.7=h14c3975_1002
  - point_cloud_utils==0.12.0=py37h9de70de_0
  - libcurl==7.62.0=h20c2e04_0
  - cffi==1.13.1=py37h2e261b9_0
  - libuuid==2.32.1=h14c3975_1000
  - libxml2==2.9.9=h13577e0_2
  - gettext==0.19.8.1=hc5be6a0_1002
  - gstreamer==1.14.5=h36ae1b5_0
  - tbb==2019.9=hc9558a2_0
  - xorg-libxdmcp==1.1.3=h516909a_0
  - intel-openmp==2019.4=243
  - libedit==3.1.20181209=hc058e9b_0
  - curl==7.62.0=hbc83047_0
  - mkl_fft==1.0.15=py37ha843d7b_0

I have updated conda.

win 10 64 bit
Python 3.8.6 | packaged by conda-forge | (default, Oct 7 2020, 18:22:52) [MSC v.1916 64 bit (AMD64)] on win32

Ground truth surface data

Hi,

thank you for making this great project available!

May I ask where you found the ground truth surfaces of the benchmark (Gargoyle, Quasimoto, Daratech etc.)?

I would like to try out samplings with different scanner options applied to the models, but for this I need the ground truth surfaces.

It seems like they are not directly provided in the benchmark data.

Runtime Error: CUDA out of Memory

Hello I am
Running this code on a single gpu GTX 1080 Ti and getting this error:

RuntimeError: CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 10.92 GiB total capacity; 10.36 GiB already allocated; 3.00 MiB free; 14.60 MiB cached)

The traceback

  File "reconstruct_surface.py", line 495, in <module>
    main()
  File "reconstruct_surface.py", line 416, in main
    sum_loss.backward()
  File "/home/azad/.conda/envs/deep-geometric-prior/lib/python3.7/site-packages/torch/tensor.py", line 107, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/home/azad/.conda/envs/deep-geometric-prior/lib/python3.7/site-packages/torch/autograd/__init__.py", line 93, in backward
    allow_unreachable=True)  # allow_unreachable flag

Any thoughts on how to make it work? Should I downsample the point cloud? I used the lord_quas.ply which has the least size of all the others.

export_point_cloud.py empty?

Hi there,

Thank you for release the codes for this amazing work! I wonder why the export_point_cloud.py is empty, will you update it later or ?

Thanks,
Ryan

Quick Demo

Thanks for your impressive CVPR2019 work and kindly sharing your code. I wonder if you could provide some test data and a quick demo instruction so that we can see the result in your paper? Thanks again!

Plotting Code

Hi, thanks for sharing this codebase!

I was wondering if you could share the code for plotting? I was hoping to try and fix it, assuming it is still broken.

Colab

how can i implment this work on Colab?

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