How to Perform 3D Semantic Instance Segmentation Using 3D-SIS

Welcome to the world of 3D Semantic Instance Segmentation! Today, we’ll guide you through the steps to utilize the *3D-SIS* framework for segmenting RGB-D scans. Whether you are a beginner or have some experience, this blog will provide you with a user-friendly overview and troubleshooting tips along the way.

What is 3D-SIS?

*3D-SIS* is a framework dedicated to the task of detecting and segmenting individual objects in three-dimensional spaces using RGB-D (color and depth) images. With applications ranging from robotics to augmented reality, understanding and implementing this tool can open a multitude of possibilities.

Setting Up Your Environment

Before diving into the data and code, ensure you have set up your development environment. You will require Python and some necessary libraries.

Installation Steps

• Install the required dependencies by running:

pip install -r requirements.txt

• If you encounter CUDA or GPU-related issues, you may need to adjust specific parameters to enable CPU inference by changing the values in the lib/utils/config.py file.
• Your folder structure should be organized as follows:

-- code
   -- main.py
   -- datagen
   -- checkpoints
   -- ScanNet
   -- results

Data Generation

Data is the lifeblood of machine learning. The first step in utilizing 3D-SIS is to gather the necessary training and validation data. You can find the data generation code detailed in the datagen folder.

Downloading Training Data

The following links will take you to the training data you need:

• ScanNet Training Chunks (70GB)
• ScanNet Training Images (13GB)

Downloading Validation and Test Data

For validation and testing, download the following datasets:

• ScanNetV2 Benchmark Test Data (801MB)
• ScanNetV2 Validation Data (746MB)
• ScanNetV2 Validation Data (3664MB)
• SUNCG Test Data (1355MB)

Understanding the Code: An Analogy

Consider setting up a new kitchen to prepare your favorite recipe. The ingredients (your data) need to be sourced first, while the tools (the code) must be organized. Just as you would gather pots, pans, and utensils before starting to cook, you must assemble all necessary datasets and organize the code structure. Just like adjusting recipes for different dishes, the configuration files allow you to modify parameters for your specific segmentation tasks.

Running the Demo Code

Once you have everything set up, you can run the demo code using:

bash example.sh

The output will be stored in example_result/visualization.

Troubleshooting Common Issues

If you run into any problems, consider these solutions:

• Ensure all data paths are correctly set in your configuration files.
• If CUDA errors still persist, modifying the MAX_VOLUME and MAX_IMAGE parameters in lib/utils/config.py to 0 may resolve the issue.
• For further assistance, feel free to reach out to us for insights, updates, or to collaborate on AI development projects through fxis.ai.

Successful setup and execution depend on the attention given to these details. If you find any other issues, consider checking online forums or our resources at 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 may your segmentation projects be fruitful!