O-CNN: Octree-based Convolutional Neural Networks

By Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun and Xin Tong.

Internet Graphics Group, Microsoft Research Asia.

Introduction

This repository contains the implementation of O-CNN introduced in our Siggraph 2017 paper "O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis". The code is released under the MIT license.

Citation

If you use our code or models, please cite our paper.

@article {Wang-2017-OCNN,
    title     = {O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis},
    author    = {Wang, Peng-Shuai and Liu, Yang and Guo, Yu-Xiao and Sun, Chun-Yu and Tong, Xin},
    journal   = {ACM Transactions on Graphics (SIGGRAPH)},
    volume    = {36},
    number    = {4},
    year      = {2017},
}

Installation

O-CNN

O-CNN is built upon the Caffe framework and it supports octree-based convolution, deconvolution, pooling, and unpooling. The code has been tested on the Windows and Linux platforms (window 10 and Ubuntu 16.04), . Its installation is as follows:

Clone Caffe with revision 6bfc5ca8f7c2a4b7de09dfe7a01cf9d3470d22b3
The code for O-CNN is contained in the directory caffe. Clone and put it into the Caffe directory.
Follow the installation instructions of Caffe to build the code to get the executive files caffe.exe and convert_octree_data.exe etc.

Octree input for O-CNN

Our O-CNN takes the octree representation of 3D objects as input. The efficient octree data structure is described in our paper. For convenience, we provide a reference implementation to convert the point cloud with oriented normal to our octree format. The code is contained in the directory octree, along with the Microsoft Visual studio 2015 solution file, which can be built to obtain the executable file octree.exe.

O-CNN in Action

The experiments in our paper can be reproduced as follows.

Data preparation

For achieving better performance, we store all the octree inputs in a leveldb or lmdb database. Here are the details how to generate databases for O-CNN.

Download and unzip the corresponding 3D model dataset (like the ModelNet40 dataset) into a folder.
Convert all the models (in OBJ/OFF format) to dense point clouds with normals (in POINTS format). As detailed in our paper, we build a virtual scanner and shoot rays to calculate the intersection points and oriented normals. The executable files and source code can be downloaded here.
Run the tool octree.exe to convert point clouds into the octree files.

Usage: Octree <filelist> [depth] [full_layer] [displacement] [augmentation] [segmentation]

filelist: a text file whose each line specifies the full path name of a POINTS file

depth: the maximum depth of the octree tree

full_layer: which layer of the octree is full. suggested value: 2

displacement: the offset value for handing extremely thin shapes: suggested value: 0.5

segmentation: a boolean value indicating whether the output is for the segmentation task.
Convert all the octrees into a lmdb or leveldb database by the tool convert_octree_data.exe.

O-CNN for Shape Classification

The instruction how to run the shape classification experiment:

Download the ModelNet40 dataset, and convert it to a lmdb database as described above. Here we provide a lmdb database with 5-depth octrees for convenience.
Download the O-CNN protocol buffer files, which are contained in the folder caffe/examples/o-cnn.
Configure the path of the database and run caffe.exe according to the instructions of Caffe. We also provide our pre-trained Caffe model in caffe/examples/o-cnn.

O-CNN for Shape Retrieval

The instruction how to run the shape retrieval experiment:

Download the dataset from SHREC16, and convert it to a lmdb database as described above.
Follow the same approach as the classification task to train the O-CNN.
In the retrieval experiment, the orientation pooling is used to achieve better performance. The code caffe/tools/feature_pooling.cpp can be used to fulfill the task. We will provide the automated tool soon.
The retrieval result can be evaluated by the javascript code provided by SHREC16.

O-CNN for Shape Segmentation

The instruction how to run the segmentation experiment:

The original part annotation data is provided as the supplemental material of the work "A Scalable Active Framework for Region Annotation in 3D Shape Collections". As detailed in Section 5.3 of our paper, the point cloud in the original dataset is relatively sparse and the normal information is missing. We convert the sparse point clouds to dense points with normal information and correct part annotation. Here is one converted dataset for your convenience, and we will release more data soon.
convert the dataset to a lmdb database.
Download the protocol buffer files, which are contained in the folder caffe/examples/o-cnn.
For CRF refinement, please refer to the code provided here. We will provide the automated tool soon.

Acknowledgments

We thank the authors of ModelNet, ShapeNet and Region annotation dataset for sharing their 3D model datasets with the public.

Contact

Please contact us (Pengshuai Wang wangps@hotmail.com, Yang Liu yangliu@microsoft.com ) if you have any problem about our implementation or request to access all the datasets.

isunchy / O-CNN