Datasets:
Enhance dataset card: Add descriptive tags and detailed sample usage
Browse filesThis PR enhances the dataset card by:
- Adding more descriptive `tags` (`3d-physics`, `material-properties`, `gaussian-splatting`, `clip-features`, `3d-assets`) to the metadata for better discoverability. These tags are derived from the paper's abstract and the project's GitHub README, highlighting key aspects of the dataset and its applications.
- Expanding the "Sample Usage" section to include a Python code snippet demonstrating how to use the dataset and models with the `pipeline.py` script from the associated GitHub repository. This provides a more complete and actionable example of using the artifact beyond just data download.
README.md
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---
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task_categories:
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- image-to-3d
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-
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---
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# Pixie Dataset
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## Sample Usage
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-
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```bash
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python scripts/download_data.py
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```
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-
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## Citation
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---
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license: mit
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task_categories:
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- image-to-3d
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tags:
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- 3d-physics
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- material-properties
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- gaussian-splatting
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- clip-features
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- 3d-assets
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---
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# Pixie Dataset
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## Sample Usage
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First, use the download script in the Pixie repository to automatically download this data and models:
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```bash
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python scripts/download_data.py
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```
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Then, you can run the main pipeline with a synthetic Objaverse object, for example:
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```python
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python pipeline.py obj_id=f420ea9edb914e1b9b7adebbacecc7d8 material_mode=neural
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```
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This command will:
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1. Download the specified Objaverse asset.
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2. Render it and train 3D representations (NeRF, Gaussian Splatting).
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3. Generate a voxel feature grid.
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4. Use the trained neural networks to predict the physics field.
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5. Run the MPM physics solver using the predicted physics parameters.
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For more detailed usage, including real-scene processing and training, refer to the [Github repository's usage section](https://github.com/vlongle/pixie#usage).
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## Citation
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