Add dataset card for ARSG-110K

Hi, I'm Niels from the community science team at Hugging Face. This PR adds a dataset card for ARSG-110K. This dataset was introduced as part of the 3D-Fixer paper and represents a significant contribution to scene-level 3D generation. The PR includes:
- Metadata for the `image-to-3d` task category and license.
- Links to the original paper, project page, and code repository.
- A summary of the dataset's scale and features.
- The BibTeX citation for the paper.

README.md

@@ -1,3 +1,34 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- image-to-3d
+---
+
+# ARSG-110K
+
+ARSG-110K is a large-scale scene-level dataset for 3D scene generation and completion, introduced in the paper [3D-Fixer: Coarse-to-Fine In-place Completion for 3D Scenes from a Single Image](https://huggingface.co/papers/2604.04406).
+
+- **Project Page:** [https://zx-yin.github.io/3dfixer](https://zx-yin.github.io/3dfixer)
+- **Github Repository:** [https://github.com/HorizonRobotics/3D-Fixer](https://github.com/HorizonRobotics/3D-Fixer)
+- **Paper:** [https://arxiv.org/abs/2604.04406](https://arxiv.org/abs/2604.04406)
+
+## Dataset Summary
+
+ARSG-110K is designed to address the data scarcity bottleneck in compositional 3D scene generation. It is one of the largest scene-level datasets to date, comprising:
+- Over **110,000** diverse scenes.
+- **3 million** annotated images.
+- High-fidelity 3D ground truth, including object-level layouts and annotations.
+- Built upon the [TRELLIS-500K](https://github.com/microsoft/TRELLIS/blob/main/DATASET.md) dataset.
+
+The dataset supports tasks such as in-place completion, where complete 3D assets are generated conditioned on partially visible point clouds cropped from fragmented geometry.
+
+## Citation
+
+```bibtex
+@inproceedings{yin2026tdfixer,
+  title={3D-Fixer: Coarse-to-Fine In-place Completion for 3D Scenes from a Single Image},
+  author={Yin, Ze-Xin and Liu, Liu and Wang, Xinjie and Sui, Wei and Su, Zhizhong and Yang, Jian and Xie, jin},
+  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
+  year={2026}
+}
+```