INSTALL.md

Installation instructions for Ubuntu 16.04

• Make sure CUDA and cuDNN are installed. Three configurations have been tested:

  • TensorFlow 1.4.1, CUDA 8.0 and cuDNN 6.0
  • TensorFlow 1.12.0, CUDA 9.0 and cuDNN 7.3.1, gcc/g++ 4.8, Python 3.6.9
  • TensorFlow 1.12.0, CUDA 9.0 and cuDNN 7.4
  • TensorFlow 1.13.0, CUDA 10.0 and cuDNN 7.5 (bug found only with this version).

• Tested on a RTX 2080 Ti. Driver version: 450.80.02

• Ensure all python packages are installed :

      sudo apt update
      sudo apt install python3-dev python3-pip python3-tk
  

• Follow Tensorflow installation procedure.

• Install the other dependencies with pip:

  • numpy
  • scikit-learn
  • psutil
  • matplotlib (for visualization)
  • mayavi (for visualization)
  • PyQt5 (for visualization)
  • Open3D (for point cloud I/O)
  • bpy (for rendering depth images via blender)
  • OpenEXR & Imath
  • h5py==2.9.0
  • pandas==0.24.2
  • transforms3d==0.3.1
  • seaborn

• Build the distance cuda kernels in pc_distance. Open a terminal in this folder, and run:

      make
  

• Compile the customized Tensorflow operators located in tf_custom_ops. Open a terminal in this folder, and run:

      sh compile_op.sh
  

  N.B. If you installed Tensorflow in a virtual environment, it needs to be activated when running these scripts

• Compile the C++ extension module for python located in cpp_wrappers. Open a terminal in this folder, and run:

      sh compile_wrappers.sh
  

You should now be able to train Kernel-Point Convolution models

Installation instructions for Ubuntu 18.04 (Thank to @noahtren)

• Remove the -D_GLIBCXX_USE_CXX11_ABI=0 flag for each line in tf_custom_ops/compile_op.sh (problem with the version of gcc). One configuration has been tested:

  • TensorFlow 1.12.0, CUDA 9.0 and cuDNN 7.3.1