Update README.md

README.md

@@ -1,101 +1,101 @@
----
-license: other
-task_categories:
-- depth-estimation
-- image-to-3d
-tags:
-- depth-estimation
-- benchmark
-- evaluation
-- multi-view-stereo
-- 3d-reconstruction
-- geometry
-- camera-pose-estimation
-size_categories:
-- 10K<n<100K
-language:
-- en
-pretty_name: DA3-BENCH - Depth Anything 3 Evaluation Benchmark
----
-
-# DA3-BENCH: Depth Anything 3 Evaluation Benchmark
-
-This repository contains processed benchmark datasets for evaluating [Depth Anything 3](https://depth-anything-3.github.io/) depth estimation and visual geometry models. The datasets are provided in a convenient, ready-to-use format for research and evaluation purposes.
-
-## About Depth Anything 3
-
-**Depth Anything 3** (DA3) is a state-of-the-art model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses. It achieves superior performance in:
-
-- **Monocular Depth Estimation**: Outperforms Depth Anything 2 with better detail and generalization
-- **Camera Pose Estimation**: 35.7% improvement over prior SOTA
-- **Multi-View Geometry**: 23.6% improvement in geometric accuracy
-- **3D Gaussian Splatting**: Superior rendering quality from arbitrary visual inputs
-
-For more details, visit the [official project page](https://depth-anything-3.github.io/).
-
-## 📦 Included Datasets
-
-The benchmark includes the following datasets, each compressed as a separate zip file:
-
-| Dataset | Size | Description |
-|---------|------|-------------|
-| **7scenes.zip** | 3.4 GB | 7-Scenes indoor localization dataset |
-| **dtu.zip** | 8.3 GB | DTU Multi-View Stereo dataset |
-| **dtu64.zip** | 1.7 GB | DTU 64-view subset |
-| **eth3d.zip** | 15 GB | ETH3D high-resolution multi-view dataset |
-| **hiroom.zip** | 683 MB | High-resolution indoor room scenes |
-| **scannetpp.zip** | 11 GB | ScanNet++ indoor scene understanding dataset |
-
-**Total Size**: ~40 GB
-
-## 🚀 Usage
-
-Each dataset has been preprocessed and structured for convenient use in depth estimation evaluation pipelines. Simply download and extract the dataset(s) you need.
-
-```bash
-# Download from Hugging Face (example)
-huggingface-cli download depth-anything/DA3-BENCH 7scenes.zip --repo-type dataset
-
-# Extract a dataset
-unzip 7scenes.zip
-```
-
-## ⚖️ License and Citation
-
-**IMPORTANT:** These datasets are provided in a processed format for convenience. Users **must strictly follow the original usage licenses** of each respective dataset:
-
-- **7-Scenes**: [Microsoft Research License](https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/)
-- **DTU MVS**: [DTU Dataset License](https://roboimagedata.compute.dtu.dk/)
-- **ETH3D**: [ETH3D Dataset Terms](https://www.eth3d.net/)
-- **ScanNet++**: [ScanNet Dataset License](http://www.scan-net.org/)
-
-### Citing Depth Anything 3
-
-If you use this benchmark, please cite the Depth Anything 3 paper:
-
-```bibtex
-@article{lin2025depthanything3,
-  title={Depth Anything 3: Recovering the Visual Space from Any Views},
-  author={Haotong Lin and Sili Chen and Jun Hao Liew and Donny Y. Chen and Zhenyu Li and Guang Shi and Jiashi Feng and Bingyi Kang},
-  journal={arXiv preprint},
-  year={2025}
-}
-```
-
-### Citing Original Datasets
-
-Additionally, please cite the respective original dataset papers for each benchmark you use. Refer to the original dataset websites for proper citation information.
-
-## 📧 Contact
-
-For questions about:
-- **Processed datasets**: Please open an issue in this repository
-- **Depth Anything 3 model**: Visit the [official project page](https://depth-anything-3.github.io/) or [GitHub repository](https://github.com/DepthAnything/Depth-Anything-V3)
-
-## 🙏 Acknowledgements
-
-We thank the authors of the original datasets for making their data publicly available for research purposes, and the Depth Anything team for developing this state-of-the-art depth estimation framework.
-
----
-
-**Disclaimer**: This is a processed collection for evaluation purposes only. All rights to the original data belong to the respective dataset creators. Users must obtain proper permissions and follow all applicable licenses when using these datasets.
+---
+license: other
+task_categories:
+- depth-estimation
+- image-to-3d
+tags:
+- depth-estimation
+- benchmark
+- evaluation
+- multi-view-stereo
+- 3d-reconstruction
+- geometry
+- camera-pose-estimation
+size_categories:
+- 10K<n<100K
+language:
+- en
+pretty_name: DA3-BENCH - Depth Anything 3 Evaluation Benchmark
+---
+
+# DA3-BENCH: Depth Anything 3 Evaluation Benchmark
+
+This repository contains processed benchmark datasets for evaluating [Depth Anything 3](https://depth-anything-3.github.io/) depth estimation and visual geometry models. The datasets are provided in a convenient, ready-to-use format for research and evaluation purposes.
+
+## About Depth Anything 3
+
+**Depth Anything 3** (DA3) is a state-of-the-art model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses. It achieves superior performance in:
+
+- **Monocular Depth Estimation**: Outperforms Depth Anything 2 with better detail and generalization
+- **Camera Pose Estimation**: 35.7% improvement over prior SOTA
+- **Multi-View Geometry**: 23.6% improvement in geometric accuracy
+- **3D Gaussian Splatting**: Superior rendering quality from arbitrary visual inputs
+
+For more details, visit the [official project page](https://depth-anything-3.github.io/).
+
+## 📦 Included Datasets
+
+The benchmark includes the following datasets, each compressed as a separate zip file:
+
+| Dataset | Size | Description |
+|---------|------|-------------|
+| **7scenes.zip** | 3.4 GB | 7-Scenes indoor localization dataset |
+| **dtu.zip** | 8.3 GB | DTU Multi-View Stereo dataset |
+| **dtu64.zip** | 1.7 GB | DTU 64-view subset |
+| **eth3d.zip** | 15 GB | ETH3D high-resolution multi-view dataset |
+| **hiroom.zip** | 683 MB | High-resolution indoor room scenes |
+| **scannetpp.zip** | 11 GB | ScanNet++ indoor scene understanding dataset |
+
+**Total Size**: ~40 GB
+
+## 🚀 Usage
+
+Each dataset has been preprocessed and structured for convenient use in depth estimation evaluation pipelines. Simply download and extract the dataset(s) you need.
+
+```bash
+# Download from Hugging Face (example)
+huggingface-cli download depth-anything/DA3-BENCH 7scenes.zip --repo-type dataset
+
+# Extract a dataset
+unzip 7scenes.zip
+```
+
+## ⚖️ License and Citation
+
+**IMPORTANT:** These datasets are provided in a processed format for convenience. Users **must strictly follow the original usage licenses** of each respective dataset:
+
+- **7-Scenes**: [Microsoft Research License](https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/)
+- **DTU MVS**: [DTU Dataset License](https://roboimagedata.compute.dtu.dk/)
+- **ETH3D**: [ETH3D Dataset Terms](https://www.eth3d.net/)
+- **ScanNet++**: [ScanNet Dataset License](http://www.scan-net.org/)
+
+### Citing Depth Anything 3
+
+If you use this benchmark, please cite the Depth Anything 3 paper:
+
+```bibtex
+@article{depthanything3,
+  title={Depth Anything 3: Recovering the Visual Space from Any Views},
+  author={Haotong Lin and Sili Chen and Jun Hao Liew and Donny Y. Chen and Zhenyu Li and Guang Shi and Jiashi Feng and Bingyi Kang},
+  journal={arXiv preprint},
+  year={2025}
+}
+```
+
+### Citing Original Datasets
+
+Additionally, please cite the respective original dataset papers for each benchmark you use. Refer to the original dataset websites for proper citation information.
+
+## 📧 Contact
+
+For questions about:
+- **Processed datasets**: Please open an issue in this repository
+- **Depth Anything 3 model**: Visit the [official project page](https://depth-anything-3.github.io/) or [GitHub repository](https://github.com/DepthAnything/Depth-Anything-V3)
+
+## 🙏 Acknowledgements
+
+We thank the authors of the original datasets for making their data publicly available for research purposes, and the Depth Anything team for developing this state-of-the-art depth estimation framework.
+
+---
+
+**Disclaimer**: This is a processed collection for evaluation purposes only. All rights to the original data belong to the respective dataset creators. Users must obtain proper permissions and follow all applicable licenses when using these datasets.