File size: 2,617 Bytes
1184ec5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
# 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)
|