FoundationPose Execution Guide

This page provides a guide to running the FoundationPose model easily and summarizes potential issues that may arise. Hope this helps!

It is assumed that the official FoundationPose repository has already been set up, including the Docker configuration and weight files.

📂 Required Data for Model-based Execution

To run this model using the Model-based (CAD model available) approach, you need to prepare the following five types of data.

1️⃣ RGB & Depth

• RGB Image: Standard RGB images in .png format, saved with matching timestamps.
• Depth Image: Corresponding depth images with the same timestamps as the RGB images.

⚠️ Important Notes

• I tested with an 800×800 resolution dataset.
  For reference, When running with 1920×1080 resolution, even an RTX 3090 experienced memory issues(24GB).
• The script data_saver.py can be used to subscribe to ROS topics and save RGB and Depth images in the specified directory.
• The topic names depend on the sensor you are using, and you should adjust the save path accordingly.
• The RGB and depth images must have identical filenames and timestamps.

2️⃣ Mask

Once the RGB and Depth images are prepared, a mask is required.

• If you intend to use the model in real-time, masks can be obtained through other methods.
• However, in this model, pose estimation is performed only for the first scene, and the system switches to tracking mode afterward.
• Therefore, you only need to generate a mask for the first frame.

How to Create a Mask

• You can use your own segmentation model to generate masks and save them as shown in the example image.
• Alternatively, you can manually create masks using the masking.py script provided in this repository.

⚠️ Important Notes

• Ensure that the saved image path matches the expected directory structure for the model.

3️⃣ CAD Model

You will need a CAD model that matches your object of interest.
However, the key point is that this model uses meters as the unit for CAD models.

⚠️ Important Notes

• If you are using Blender, make sure to apply a 1000x scaling to convert from millimeters to meters.

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4️⃣ cam_K.txt (Camera Intrinsics)

The file cam_K.txt should store the intrinsic parameters of the camera being used. If you are using a simulation environment, you can retrieve these parameters by running the following command:

rostopic echo /camera_info

For a real-world setup, you will need to go through a camera calibration process to obtain these values.

Final Steps

Once these steps are completed, all necessary data is prepared. In the run_demo script, use argparse to specify the required file paths. This step is straightforward.

https://github.com/user-attachments/assets/ac16a72d-b885-4707-97c7-47e5ce16f003

If you encounter any issues, feel free to reach out. I will assist as much as possible.

Contact: cseklee234@naver.com