Add model card and pipeline tag

README.md

@@ -1,3 +1,54 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: image-to-3d
+---
+
+# Lite3R: A Model-Agnostic Framework for Efficient Feed-Forward 3D Reconstruction
+
+Official implementation of **Lite3R**, a model-agnostic framework for efficient feed-forward 3D reconstruction from multi-view images.
+
+Lite3R introduces a systematic approach to compress large-scale 3D reconstruction models while maintaining reconstruction quality. The framework combines Sparse Linear Attention (SLA), FP8-Aware Quantization-Aware Training (QAT), and Partial Attention Distillation.
+
+[**Paper**](https://huggingface.co/papers/2605.11354) | [**Website**](https://aigeeksgroup.github.io/Lite3R/) | [**Code**](https://github.com/AIGeeksGroup/Lite3R)
+
+## Installation
+
+```bash
+# Clone the repository
+git clone https://github.com/AIGeeksGroup/Lite3R.git
+cd Lite3R
+
+# Create conda environment
+conda create -n lite3r python=3.10
+conda activate lite3r
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+## Sample Usage
+
+### Inference
+
+After downloading the model checkpoints from this repository, you can run inference using the following command:
+
+```bash
+python inference.py \
+  --model vggt \
+  --checkpoint checkpoints/fp8_qat_1ep/vggt/vggt_fp8_qat_1ep.pt \
+  --input_dir examples/input \
+  --output_dir examples/output
+```
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@article{zhang2026lite3r,
+  title={Lite3R: A Model-Agnostic Framework for Efficient Feed-Forward 3D Reconstruction},
+  author={Zhang, Haoyu and Zhang, Zeyu and Zhou, Zedong and Zhao, Yang and Tang, Hao},
+  journal={arXiv preprint arXiv:2605.11354},
+  year={2026}
+}
+```