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PyTorch implementation of "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593
| Date | Stars |
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| 2026-07-24 | 273 |
| 2026-07-25 | 273 |
| 2026-07-28 | 273 |
| 2026-07-30 | 273 |
| 2026-07-31 | 273 |
| 2026-08-06 | 273 |
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<a href="https://colab.research.google.com/github/nikitakaraevv/pointnet/blob/master/nbs/PointNetClass.ipynb" target="_parent\"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> # PointNet PyTorch implementation of "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593 <img src="images/cover.gif" alt="pointnet" /> Key points of the implementation are explained in details in [this](https://towardsdatascience.com/deep-learning-on-point-clouds-implementing-pointnet-in-google-colab-1fd65cd3a263) Medium article. ## Classification dataset This code implements object classification on [ModelNet10](https://modelnet.cs.princeton.edu) dataset. As in the original paper, we sample 1024 points on objects surfaces depending on the area of the current face. Then we normalize the object to a unit sphere and add Gaussian noise. This is an example of input to the neural network that represents a chair: <img src="images/chair.gif" alt="matching points" width="400"/> You can download the dataset by following [this link](https://drive.google.com/open?id=12Mv19pQ84VO8Av50hUXTixSxd5NDjeEB) ## Classification performance | Class (Accuracy) | Overall | Bathtub | Bed| Chair|Desk|Dresser|Monitor|Night stand|Sofa|Table|Toilet| | :---: |:---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | ModelNet10 | 82.0% | 93.4% | 92.0% | 97.2% | 81.5% | 71.0% | 89.4% | 56.0% |86.9%| 93.4% |95.9%| Pretrained model is available [here](https://drive.google.com/open?id=1nDG0maaqoTkRkVsOLtUAR9X3kn__LMSL) ## Usage * The first and the best option is to run the notebook with comments and visualizations `/nbs/PointNetClass.ipynb` in Google Colab. * The second option is to clone the repository on a local machine and run a model with default parameters: ```bash git clone https://github.com/nikitakaraevv/pointnet wget http://3dvision.princeton.edu/projects/2014/3DShapeNets/ModelNet10.zip unzip -q ModelNet10.zip cd pointnet/ python train.py ``` If for some reason it doesn't work, you can install the requirements before running `python train.py`: ```bash conda create -n env python=3.7 conda activate env pip install -r requirements.txt ``` Another example of running a model is: ```python train.py --root_dir ../ModelNet10/ --batch_size 16 --lr 0.0001 --epochs 30 --save_model_path ./ckpts``` ## Part segmentation dataset The dataset includes 2609 point clouds representing different airplanes, where every point has its coordinates in 3D space and a label of an airplane’s part the point belongs to. As all images have different number of points and PyTorch library functions require images of the same size to form a PyTorch tensor, we sample uniformly 2000 points from every point cloud. You can download the dataset by following [this link](https://drive.google.com/drive/u/1/folders/1Z5XA4uJpA86ky0qV1AVgA_G1_ETkq9En) ## Part segmentation performance The resulting accuracy on the validation dataset is 88%. In the original paper part segmentation results corresponding to category of objects (airplanes) is 83.4%. ## Usage This part of the project is still in development. However, you already can run the notebook `/nbs/PointNetSeg.ipynb` in Colab. <img src="images/airplane.gif" alt="matching points" width="400"/> ## Authors * [Nikita Karaev](https://github.com/nikitakaraevv) * [Irina Nikulina](https://github.com/washburn125)
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6484f078439b1014, topic:deep-learning, topic:pytorch
matched fp:6484f078439b1014, topic:computer-vision