# 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)