Add pipeline tag and improve model card

README.md

@@ -1,22 +1,38 @@
 ---
-library_name: transformers
-license: cc-by-sa-4.0
-datasets:
-- zx1239856/3d-front-ar-packed
 base_model:
 - zx1239856/edgerunner
+datasets:
+- zx1239856/3d-front-ar-packed
+library_name: transformers
+license: cc-by-sa-4.0
+pipeline_tag: image-to-3d
 ---
 
-# Model Card for Model ID
+# PixARMesh: Autoregressive Mesh-Native Single-View Scene Reconstruction
 
-PixARMesh based on EdgeRunner
+PixARMesh is a mesh-native autoregressive framework for single-view 3D scene reconstruction. Instead of reconstructing via intermediate volumetric or implicit representations, PixARMesh directly models instances with native mesh representation. Object poses and meshes are predicted in a unified autoregressive sequence.
 
 ## Model Details
 
-PixARMesh is a mesh-native autoregressive framework for single-view 3D scene reconstruction.
-Instead of reconstructing via intermediate volumetric or implicit representations, PixARMesh directly models instances with native mesh representation. Object poses and meshes are predicted in a unified autoregressive sequence.
-
-### Model Sources
+Reconstructing complete 3D indoor scenes from a single RGB image is a complex task. PixARMesh jointly predicts object layout and geometry within a unified model, producing coherent and artist-ready meshes in a single forward pass. By augmenting a point-cloud encoder with pixel-aligned image features and global scene context via cross-attention, the model enables accurate spatial reasoning. Scenes are generated autoregressively from a unified token stream containing context, pose, and mesh, yielding compact meshes with high-fidelity geometry.
 
 - **Repository:** https://github.com/mlpc-ucsd/PixARMesh
-- **Paper:** https://arxiv.org/pdf/2603.05888
+- **Paper:** [arXiv:2603.05888](https://arxiv.org/abs/2603.05888)
+- **Project Page:** https://mlpc-ucsd.github.io/PixARMesh/
+
+## Citation
+
+If you find PixARMesh useful in your research, please consider citing:
+
+```bibtex
+@article{zhang2026pixarmesh,
+  title={PixARMesh: Autoregressive Mesh-Native Single-View Scene Reconstruction},
+  author={Zhang, Xiang and Yoo, Sohyun and Wu, Hongrui and Li, Chuan and Xie, Jianwen and Tu, Zhuowen},
+  journal={arXiv preprint arXiv:2603.05888},
+  year={2026}
+}
+```
+
+## Acknowledgements
+
+PixARMesh builds upon several excellent open-source projects including [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything), [Depth Pro](https://github.com/apple/ml-depth-pro), [DINOv2](https://github.com/facebookresearch/dinov2), and weights from [EdgeRunner](https://github.com/NVlabs/EdgeRunner) and [BPT](https://github.com/Tencent-Hunyuan/bpt). We also use physically-based renderings from the [3D-FRONT](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset) scenes.