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DGCNN-MindSpore

This repo is the MindSpore implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)(https://arxiv.org/pdf/1801.07829). Our code skeleton is borrowed from WangYueFt/dgcnn.

 

Requirements

  • Ubuntu 20.04
  • Python 3.7
  • MindSpore 1.8.1
  • CUDA 11.1
  • Package: h5py, sklearn, plyfile

 

Point Cloud Classification

Run the training script:

python dgcnn_cls_train.py

Run the evaluation script after training finished:

python dgcnn_cls_eval.py

Performance:

ModelNet40 dataset

Mean Class Acc Overall Acc
Paper (1024 points) 90.2 92.9
This repo (1024 points)

 

Point Cloud Part Segmentation

Run the training script:

python dgcnn_seg_train.py

Run the evaluation script after training finished:

python dgcnn_seg_eval.py

Performance:

ShapeNet part dataset

Mean IoU Airplane Bag Cap Car Chair Earphone Guitar Knife Lamp Laptop Motor Mug Pistol Rocket Skateboard Table
Shapes 2690 76 55 898 3758 69 787 392 1547 451 202 184 283 66 152 5271
Paper 85.2 84.0 83.4 86.7 77.8 90.6 74.7 91.2 87.5 82.8 95.7 66.3 94.9 81.1 63.5 74.5 82.6
This repo

Ackonwledgements:

We thank a lot for the official repo of WangYueFt/dgcnn.

License:

The code is released under MIT License(See LICENSE file for details)

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