Welcome to the exciting world of MeshCNN, a cutting-edge deep learning framework designed for 3D triangular meshes. This powerful neural network can be utilized for various tasks including 3D shape classification and segmentation. In this guide, we will walk you through the installation process and how to run 3D shape classification and segmentation tasks with ease.

Key Features of MeshCNN

• Handles 3D triangular meshes using convolution and pooling layers directly on mesh edges.
• Supports tasks such as 3D shape classification and segmentation.
• Includes training loss visualization through TensorBoard.

Getting Started

Installation

Follow these steps to set up MeshCNN:

1. Clone the repository:

git clone https://github.com/ranahanocka/MeshCNN.git
cd MeshCNN

2. Install the required dependencies:
• Ensure you have PyTorch v1.2 or higher.
• Optionally, install tensorboardX for visualizing training plots.
3. Create a new conda environment:

conda env create -f environment.yml

3D Shape Classification on SHREC

Here’s how to classify 3D shapes using the SHREC dataset:

1. Download the dataset:

bash ./scripts/shrec/get_data.sh

2. Run the training script (don’t forget to activate your conda environment):

bash ./scripts/shrec/train.sh

3. View training loss plots in a new terminal:

tensorboard --logdir runs

Open localhost:6006 in your browser.

4. Run the test script:

bash ./scripts/shrec/test.sh

5. Visualize collapsed edges:

bash ./scripts/shrec/view.sh

3D Shape Segmentation on Humans

To segment 3D shapes of human figures, follow a similar procedure:

1. Download the dataset:

bash ./scripts/human_seg/get_data.sh

2. Train the model:

bash ./scripts/human_seg/train.sh

3. Get pre-trained weights:

bash ./scripts/human_seg/get_pretrained.sh

4. Run the test:

bash ./scripts/human_seg/test.sh

5. Visualize results:

bash ./scripts/human_seg/view.sh

Understanding the Code: An Analogy

Think of MeshCNN as a sophisticated artist creating sculptures from a block of clay. Just as the artist carefully shapes and designs the clay using tools (convolution and pooling layers), MeshCNN manipulates 3D models (triangular meshes) to reveal their intricate designs using mathematical operations. Each operation is a step in refining the sculpture until its final form is achieved, allowing it to be classified or segmented much like categorizing different styles of art.

Troubleshooting

If you encounter any issues or have questions while setting up or running MeshCNN, please feel free to open an issue on the project page so that the team can provide support. Here are some troubleshooting tips:

• If you run into dependencies issues, ensure that your conda environment is correctly activated.
• Check if you are using the right version of PyTorch.
• For visualization problems, confirm that TensorBoard is running and accessible via your web browser.

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More Information

For additional details on segmentation and data processing, check out the MeshCNN wiki.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.