Welcome to our deep dive into the world of 3D Point Cloud Recognition! In this article, we’ll explore a project that benchmarks the robustness of 3D Point Cloud Recognition methods against common corruptions. This project is based on the paper titled “Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions” by Jiachen Sun et al.

Getting Started

Ready to jump into the project? Let’s navigate through the steps together! Here’s how you can set up the ModelNet40-C repository:

Step 1: Clone the Repository

First, you’ll want to clone the repository to your local machine. You can do this by executing the following command:

git clone --recurse-submodules git@github.com:jiachens/ModelNet40-C.git

Step 2: Install Requirements

Ensure you have the right environment. This codebase has been tested on:

• Python version: 3.7.5
• CUDA version: 10.0
• CuDNN version: 7.6
• GCC version: 5.4

We recommend using Anaconda to create a virtual environment called modelnetc:

conda create --name modelnetc python=3.7.5

Then activate it:

conda activate modelnetc

Understanding the Code

The code structure can be likened to a well-organized toolbox where every tool represents a different component of your project:

• ModelNet40-C/models: This is where the various models are stored.
• ModelNet40-C/configs: Configuration files that define the parameters for those models.
• ModelNet40-C/main.py: This file is like the project manager that controls training and testing of models.
• ModelNet40-C/dataloader.py: Think of it as a librarian, fetching the right data for each model.
• ModelNet40-C/utils.py: Home to various utility functions that put everything together smoothly.

Running Experiments

Training a Model

To train your model, use the main.py script. The command format is quite user-friendly:

python main.py --exp-config path_to_config

Just replace path_to_config with your actual config file path!

Evaluating Pre-trained Models

Feel free to dive straight into testing pre-trained models with commands that mirror the training commands but focus on evaluation, offering a simple path to results!

python main.py --entry test --model-path path_to_model.pth --exp-config config.yaml

Troubleshooting

While this setup is designed for ease, some common hiccups might arise:

• Installation Issues: If libraries don’t seem to install properly, ensure you’re in the right environment and that dependencies are still correctly defined in requirements.txt.
• CUDA Errors: If you face any issues with CUDA, verify your TORCH_CUDA_ARCH_LIST variable is correctly set for your GPU. A good starting point is:

export TORCH_CUDA_ARCH_LIST=6.0;6.1;6.2;7.0;7.5

• Rendering Issues: Remember, always have a monitor connected when using Open3D library. Background rendering can lead to unwanted errors!

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Conclusion

With this guide, you should have a clear path to harnessing the power of 3D Point Cloud Recognition. Whether you’re training models or evaluating pre-trained ones, you’re now equipped with the knowledge to explore the boundaries of this technology.

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.