Welcome to our comprehensive guide on setting up the Visual 3D Detection Package for the KITTI dataset. This package is an exciting opportunity for developers and researchers interested in visual tasks related to autonomous driving. With a modular design and state-of-the-art performance, it allows for flexible experimentation with 3D detection and various other tasks.

Key Features of the Package

• SOTA Performance: Achieve state-of-the-art results in visual 3D detection.
• Modular Design: Easily modify datasets, networks, and running pipelines.
• Support Various Tasks: Compatible with mono and stereo 3D detection as well as depth prediction.
• Distributed Training: Utilize multiple GPUs for efficient training.
• Installation-Free Setup: Keep your environment clean by avoiding unnecessary installations.
• Global Path-based IMDB: Conveniently manage data and code separately without needing data in specific folders.

Setting Up the Environment

Setting up this package is straightforward. Follow the steps below:

bash
pip3 install -r requirement.txt
# Manually check for dependencies if necessary
bash
# Build operations (like deform convs and iou3d)
make.sh

Starting Your Training

Once your environment is set up, it’s time to start training. Please refer to the documentation for specific tasks:

• Mono3D
• Stereo3D
• Depth Predictions

More demonstrations will be added over time through contributions and further paper submissions.

Config and Path Setup

For optimal performance, you will need to modify the paths and parameters in the config*.py files. Remember:

• *_examples files are templates and not utilized by the code.
• Paths that you must modify include:
• cfg.path.data_path: Path to KITTI training data.
• cfg.path.test_path: Path to KITTI testing data.
• cfg.path.visualDet3D_path: Path to the visualDet3D directory of the current repo.
• cfg.path.project_path: Working directory for project files.

Analogous Explanation of Key Elements

Imagine you’re a chef preparing a unique dish (your project) using a recipe (the code). Each ingredient represents a component of your system, like the datasets, processing functions, and models. The setup and configuration steps are akin to gathering your ingredients, chopping them, and preparing your kitchen before you start cooking. Just as a chef wouldn’t want to mix ingredients prematurely, you should adjust paths and parameters before diving into training. By keeping everything modular, just as you would keep various ingredients separate until it’s time to combine them, you ensure your dish is a culinary masterpiece, showcasing the right flavors at the right time.

Troubleshooting Tips

If you encounter any issues with the setup or training process, consider the following:

• Double-check that all paths in your config files are accurate and point to the correct folders.
• Ensure that all dependencies are properly installed. Refer to the requirement.txt file for details.
• Check GitHub issues on the repository for any known problems or similar queries from other users.

If you still face problems or want to report bugs, feel free to open an issue on the repo or reach out via email to yliuhb@connect.ust.hk.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

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. Happy coding and visual detection!