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Installation

Octree

Our O-CNN takes the octree representation of 3D objects as input. And the octree can be built from a point cloud representation of a 3D shape. The code for converting a point cloud into octree representation and some useful tools to process octrees, point clouds and meshes are contained in the folder octree, which can be built via cmake:

cd octree/external && git clone --recursive https://github.com/wang-ps/octree-ext.git
cd .. && mkdir build && cd build
cmake ..  && cmake --build . --config Release

After building, add the executive files to the system path using the following command on the Win10.

set PATH=the\absolute\path\octree\build\Release;%PATH%

Or run the following command on the Ubuntu.

export PATH=`pwd`:$PATH

Caffe

The caffe-based implementation has been tested on Windows 10 x64 and Ubuntu 16.04. To build the code, Cuda 8.0 and Cudnn 6.0 have to be installed.

  1. Clone Caffe with revision 6bfc5ca:

    git clone https://github.com/BVLC/caffe.git caffe-official
cd caffe-official && git checkout 6bfc5ca

  2. Copy the code contained in the directory caffe into the caffe-official directory to override the official Caffe code.

  3. Follow the installation instructions of Caffe to build the code to get the executive files: caffe, convert_octree_data and feature_pooling etc.

  4. NOTE: Compared with the original code used in the experiments of the O-CNN paper, the code in this repository is refactored for the readability and maintainability, with the sacrifice of speed (it is about 10% slower, but it is more memory-efficient). If you want to try the original code or do some speed comparisons with our O-CNN, feel free to drop me an email, we can share the original code with you.

Tensorflow

The code has been tested with Ubuntu 16.04/18.04 and TensorFlow 1.14.0/1.12.0.

  1. To build the code, Cuda 10.1 and Anaconda with python 3.x have to be installed.

  2. Create a new conda environment and install tensorflow-gpu 1.14.0.

    conda create -n tf-1.14.0 tensorflow-gpu==1.14.0
conda activate tf-1.14.0
conda install -c conda-forge yacs tqdm

  3. Build the code under tensorflow.

    cd tensorflow/libs
python build.py

  4. NOTE: If you install tensorflow via pip or docker instead of conda, you should install g++4.8 and rebuild the code under folders octree and tensorflow. If you get warning messages from numpy or BatchNorm, you can execute the following commands: pip install -U gast==0.2.2 numpy==1.16.4.

PyTorch

The code has been tested with Ubuntu 16.04 and PyTorch 1.6.0.

  1. Enter the subfolder pytorch, and install PyTorch and relevant packages with the following commands:

    conda create --name pytorch-1.7.0 python=3.7
conda activate pytorch-1.7.0
conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=10.2 -c pytorch
pip install -r requirements.txt

    The code is also tested with the following pytorch version:

    • pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1
    • pytorch==1.9.0 torchvision cudatoolkit=11.1

  2. Build O-CNN under PyTorch.

    python setup.py install --build_octree

  3. Run the test cases.

    python -W ignore test/test_all.py -v