One View, Many Worlds: Single-Image to 3D Object Meets Generative Domain Randomization for One-Shot 6D Pose Estimation

Zheng Geng, Nan Wang, Shaocong Xu, Bohan Li, Zhaoxi Chen, Chongjie Ye, Sida Peng, Hao Zhao

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TODO:

• Release Polished Paper
• Release Evaluation Code
• Release Training Code

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⚙️ Installation

Quick Setup Using setup.sh

To streamline the setup process, we provide a setup.sh script that automates all installation steps. Follow these instructions to get started:

Prerequisites

Before running the setup.sh script, ensure you have the following prerequisites installed on your system:

• Python 3.11+
• Conda (recommended)
• Basic build tools (git, make, cmake, etc.)

Usage

1. Clone the repository:

   git clone https://github.com/GZWSAMA/OnePoseviaGen.git
   cd OnePoseviaGen

2. Make the setup.sh script executable:

   chmod +x setup.sh

3. Run the setup.sh script:

   It is recommended to run this script within a fresh conda environment. Here's how you can create and activate a new environment before running the script:

   conda create -n OnePoseviaGen python=3.11 -y
   conda activate OnePoseviaGen
   ./setup.sh

   The script will handle:

   • Installing PyTorch with CUDA support.
   • Installing required dependencies.
   • Cloning and installing external extensions.
   • Building FoundationPose.
   • Installing local packages in editable mode.
   • Downloading pretrained weights.
   • Patching the transformers library.

4. Verify the setup:

   After running the script, verify that all dependencies are correctly installed and the necessary files are downloaded.

5. Web demo:

   python app.py

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Troubleshooting

Indices should be on the same device

Can't convert cuda:0 device type tensor to numpy

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📚 Citation

If you find this work useful, please cite:

TODO