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Скачать или смотреть 6D Pose Estimation Full Tutorial : Foundation Pose & Docker Implementation

  • Coding Tamilan
  • 2025-06-18
  • 523
6D Pose Estimation Full Tutorial : Foundation Pose & Docker Implementation
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Описание к видео 6D Pose Estimation Full Tutorial : Foundation Pose & Docker Implementation

6D Pose Estimation Tutorial : Foundation Pose & Docker Implementation
#computervision #objecttracking #deeplearning
Insta Id: vicky1626
Telegram id : codingtamilan143
Email For Business Promotion : [email protected]

📌 Theory Behind This Setup:

✅ Why Add the foundationpose Folder?
By copying the foundationpose folder directly into the Docker image, you ensure that all required scripts and dependencies are available inside the container without needing extra mounts or manual copying.
This makes the container self-contained, portable, and easy to share.

✅ Why Use Docker?
Ensures compatibility across different environments.
Avoids dependency issues on different machines.
Allows GPU acceleration using --gpus all.
Easily share your setup using a prebuilt image (viki1626/codingtamilan:latest).

✅ How 6D Pose Estimation Works in This Setup?
The Foundation Pose Model predicts object orientation and position in 3D space using only an RGB camera (no depth required).
Running inside Docker ensures consistent inference performance while utilizing GPU acceleration.

Installation Steps

1️⃣ Required Tools:
C++ Build Tools (VS Code)
CMake
Git Bash
VcXsrv Server (for GUI forwarding)

2️⃣ Build & Run Docker Container:
docker build -t foundationpose . # Build the image
docker pull viki1626/codingtamilan:latest # Pull the prebuilt image from Docker Hub

docker run --gpus all --env NVIDIA_DISABLE_REQUIRE=1 -it --network=host --name pose --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ipc=host -e DISPLAY=${DISPLAY} -e GIT_INDEX_FILE viki1626/codingtamilan:latest bash

3️⃣ Start X Server:
Make sure VcXsrv is running for GUI support.

4️⃣ Create a Folder Inside the Container & Exit:
exit
5️⃣ Re-enter the Container & Set Display:
docker exec -it pose /bin/bash
export DISPLAY=192.168.x.x:0.0 # Replace with your IP
echo $DISPLAY
6️⃣ Run the Model:
python run_demo.py

📌 Resources & Links:

FoundationPose GitHub Repository: [Link to FoundationPose GitHub]
Docker Hub Image (viki1626/codingtamilan): [Link to your Docker Hub image]
Related Research Paper (if applicable): [Link to paper]
Our Computer Vision Playlist: [Link to your playlist]
Connect with us on [Social Media Platform]: [Your Social Media Link]
🕒 Timestamps (Add these once you finalize your video structure):

0:00 Introduction & What is FoundationPose
0:55 Why use Docker for 6D Pose Estimation
2:10 Required Tools & Installation Steps Overview
3:45 Building the Docker Image
5:20 Running the Docker Container with GPU Support
7:00 Setting up X Server for GUI
8:30 Inside the Container: Environment Setup
10:15 Running the FoundationPose Demo
12:00 Troubleshooting Tips
13:30 Conclusion & Next Steps

👍 If you find this video helpful, don’t forget to like, share, and subscribe for more AI and computer vision content!

#6DPoseEstimation #FoundationPose #Docker #ComputerVision #MachineLearning #DeepLearning #AI #Robotics #PoseEstimation #ObjectTracking #NVIDIA #GPUAcceleration #TamilTutorial #TechTutorial #FoundationPoseSetup #DockerContainer #OpenCV #python
#6DPoseEstimation #SinRef6D #FoundationPose #PoseEstimation #ComputerVision #AI #MachineLearning #DeepLearning #Docker #RGBPoseEstimation #AR #Robotics #ObjectTracking #PoseDetection #AIResearch #techtutorial
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