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ACKNOWLEDGMENTS.md

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+# Acknowledgments
+
+**PySceneKit** would not be possible without the incredible work of various open-source projects and libraries that have paved the way for scene processing and visualization. I want to extend my heartfelt thanks to:
+
+## Libraries
+- **Open3D**: A modern library for 3D data processing. [link](https://www.open3d.org/)
+- **Trimesh**: Trimesh is a pure Python 3.7+ library for loading and using triangular meshes with an emphasis on watertight surfaces. [link](https://trimesh.org/)
+- **PyMeshLab**: PyMeshLab is a Python library that interfaces to MeshLab. [link](https://pymeshlab.readthedocs.io/en/latest/)
+- **Numpy**: NumPy is an open source project that enables numerical computing with Python. [link](https://numpy.org/)
+
+## 2D Scene Understanding Methods
+
+### Depth Estimation
+
+- **MiDas**: Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer. [link](https://github.com/isl-org/MiDaS)
+
+- **Depth Anything V2**: Robust and Accurate Depth Estimation for RGB images. [link](https://github.com/DepthAnything/Depth-Anything-V2)
+
+- **Metric3D**: Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image. [link](https://github.com/YvanYin/Metric3D)
+
+- **Depth Pro**: Sharp Monocular Metric Depth in Less Than a Second. [link](https://github.com/apple/ml-depth-pro)
+
+- **Lotus**: Diffusion-based Visual Foundation Model for High-quality Dense Prediction. [link](https://github.com/EnVision-Research/Lotus)
+
+### Normal Estimation
+
+- **DSINE**: Rethinking Inductive Biases for Surface Normal Estimation. [link](https://baegwangbin.github.io/DSINE/)
+
+- **StableNormal**: Reducing Diffusion Variance for Stable and Sharp Normal. [link](https://github.com/Stable-X/StableNormal)
+
+- **Lotus**: Diffusion-based Visual Foundation Model for High-quality Dense Prediction. [link](https://github.com/EnVision-Research/Lotus)
+
+### Image Segmentation
+
+- **OneFormer**: One Transformer to Rule Universal Image Segmentation. [link](https://github.com/SHI-Labs/OneFormer)
+
+- **Segment Anything**: A promptable segmentation system with zero-shot generalization to unfamiliar objects and images. [link](https://github.com/facebookresearch/segment-anything)
+
+## 3D Scene Understanding Methods
+
+### Mesh Reconstruction
+
+- **DUSt3R**: Geometric 3D Vision Made Easy. [link](https://dust3r.europe.naverlabs.com/)
+
+### Mesh Simplification
+
+- **Instant Meshes**: Instant Field-Aligned Meshes. [link](https://github.com/wjakob/instant-meshes)
+
+### Object Detection
+
+- **UniDet3D**: Multi-dataset Indoor 3D Object Detection. [link](https://github.com/3dlg-hcvc/unidet3d)
+

README.md

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 ---
 license: mit
 ---
+
+# PySceneKit
+
+PySceneKit is an open-source Python library designed for common scene processing and visualization tasks. Whether you're working with 2D or 3D scenes, PySceneKit provides a comprehensive toolkit to help you manipulate, analyze, and visualize your data with ease.
+
+## Introduction
+
+Welcome to PySceneKit! This project is fueled by my passion for scene understanding, particularly in indoor environments. Frustrated by the lack of user-friendly tools for processing images and 3D indoor scenes, I often found myself rewriting code for different datasets.
+
+PySceneKit aims to simplify scene understanding by providing an intuitive toolkit that incorporates both state-of-the-art techniques and classic methods tailored for indoor scenes. I warmly welcome contributions from fellow enthusiasts and researchers to help make this project a valuable resource for everyone!
+
+## Progress
+
+- [x] 🌟 **Kicking Off the Adventure**: Initial setup complete!
+- [ ] 💡 **Bringing Ideas to Life**: Core functionalities in the works.
+- [ ] 🎨 **Crafting Beautiful Scenes**: Building basic scene processing features.
+- [ ] 🔍 **Visual Wonderland**: Adding stunning visualization tools.
+- [ ] 📚 **Sharing the Love**: Writing documentation and creating examples.
+- [ ] 🔧 **Polishing the Gem**: Testing and optimizing for the best performance.
+- [ ] 🚀 **Launch Countdown**: Preparing for the exciting release of v1.0!
+
+
+## Acknowledgments
+
+PySceneKit would not be possible without the incredible work of various open-source projects and libraries that have paved the way for scene processing and visualization. For a detailed list of acknowledgments, please see the [ACKNOWLEDGMENTS.md](./ACKNOWLEDGMENTS.md) file.
+
+## Citation
+
+If you find PySceneKit useful in your research, please consider citing the project:
+
+```bibtex
+@misc{mao2024pyscenekit,
+    title={PySceneKit GitHub Repository},
+    author={Mao, Yongsen},
+    year={2024}
+}
+```