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Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
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Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds

Created by Huan Lei, Naveed Akhtar and Ajmal Mian

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This work is based on our Arxiv tech report, which is a significant extension of our original paper presented in IEEE CVPR2019.

We propose a spherical kernel for efficient graph convolution of 3D point clouds. Our metric-based kernels systematically quantize the local 3D space to identify distinctive geometric relationships in the data. Similar to the regular grid CNN kernels, the spherical kernel maintains translation-invariance and asymmetry properties, where the former guarantees weight sharing among similar local structures in the data and the latter facilitates fine geometric learning. The proposed kernel is applied to graph neural networks without edge-dependent filter generation, making it computationally attractive for large point clouds. In our graph networks, each vertex is associated with a single point location and edges connect the neighborhood points within a defined range. The graph gets coarsened in the network with farthest point sampling. Analogous to the standard CNNs, we define pooling and unpooling operations for our network. We demonstrate the effectiveness of the proposed spherical kernel with graph neural networks for point cloud classification and semantic segmentation using ModelNet, ShapeNet, RueMonge2014, ScanNet and S3DIS datasets.

In this repository, we release the code and trained models for classification and segmentation.


If you find our work useful in your research, please consider citing:

  title={Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds},  
  author={Lei, Huan and Akhtar, Naveed and Mian, Ajmal},  
  journal={arXiv preprint arXiv:1909.09287},  
  title={Octree guided CNN with Spherical Kernels for 3D Point Clouds},  
  author={Lei, Huan and Akhtar, Naveed and Mian, Ajmal},  
  journal={IEEE Conference on Computer Vision and Pattern Recognition},  


Our code is released under MIT License (see LICENSE file for details).


Install Tensorflow. The code was tested with Python 3.5, Tensorflow 1.12.0, Cuda 9.0 and Cudnn 7.1.4 on Ubuntu 16.04. The used GPU is NVIDIA Titan XP.
**Note: while implementing the new tensorlfow operators, we assumed that the GPU supports a block of 1024 threads.

Please compile the cuda-based operations in tf-ops folder using the command

(sudo) ./

Data Preparation

You may need to install Matlab. It is required to preprocess the datasets, such as the grid-based downsampling.
We preprocess each segmentation dataset using the corresponding function under the folder preprocessing:


And then transform the *.txt files to tfrecord format for fast data feeding in Tensorflow:

cd io


All of the trained models and our results on ShapeNet and S3DIS can be downloaded from this link.

  • ModelNet

    • To train a model to classify the 40 object classes:
      cd modelnet40_cls 
    • To test the classification results:
      python --num_votes=12  
  • ShapeNet

    • To train a model to segment parts of the Table Category:
      cd shapenet_seg   
      python --shape_name=Table 
    • To test the segmentation performance of the trained model:
      python  --shape_name=Table  --model_name=xxxx    
  • RueMonge2014

    • train
      cd ruemonge2014_seg    
    • test
      python  --model_name=xxxx    
  • ScanNet V2
    Download the ScanNet dataset.

    • train
      cd scannet_seg  
    • test
      python  --model_name=xxxx    
  • S3DIS
    Download the S3DIS dataset.

    • train
      cd s3dis_seg  
    • test
      python --model_name=xxxx    


The datasets are trained and tested with split blocks. We merge them back into complete scenes using functions under the folder post-merging in Matlab.

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