ocnn 2.1.1

Octree-based Sparse Convolutional Neural Networks

O-CNN

Documentation

This repository contains the pure PyTorch-based implementation of O-CNN. The code has been tested with Pytorch>=1.9.0.

O-CNN is an octree-based sparse convolutional neural network framework for 3D deep learning. O-CNN contains the CNN storage and computation into non-empty sparse voxels for efficiency and uses the octree data structure to organize and index these sparse voxels.

The concept of sparse convolution in O-CNN is the same with H-CNN, SparseConvNet, and MinkowskiNet. The key difference is that our O-CNN uses the octree to index the sparse voxels, while these 3 works use the Hash Table.

Our O-CNN is published in SIGGRAPH 2017, H-CNN is published in TVCG 2018, SparseConvNet is published in CVPR 2018, and MinkowskiNet is published in CVPR 2019. Therefore, the idea of constraining CNN computation into sparse non-emtpry voxels is first proposed by our O-CNN. Currently, this type of 3D convolution is known as Sparse Convolution in the research community.

Key benefits of ocnn-pytorch

• Simplicity. The ocnn-pytorch is based on pure PyTorch, it is portable and can be intalled with a simple command:pip install ocnn. Other sparse convolution frameworks heavily rely on C++ and CUDA, and it complicated to configure the compiling environment.

• Efficiency. The ocnn-pytorch is very efficient compared with other sparse convolution frameworks. It only takes 18 hours to train the network on ScanNet for 600 epochs with 4 V100 GPUs. For reference, under the same training settings, MinkowskiNet 0.4.3 takes 60 hours and MinkowskiNet 0.5.4 takes 30 hours.