Update README.md

README.md

@@ -7,7 +7,7 @@ size_categories:
 # Procedural 3D Synthetic Shapes Dataset
 
 ## Overview
-This dataset contains 152,508 procedurally synthesized 3D shapes in order to help people better reproduce results for [Learning 3D Representations from Procedural 3D Programs](). The shapes are created using a procedural 3D program that combines primitive shapes (e.g., cubes, spheres, and cylinders) and applies various transformations and augmentations to enhance geometric diversity.
+This dataset contains 152,508 procedurally synthesized 3D shapes in order to help people better reproduce results for [Learning 3D Representations from Procedural 3D Programs](https://arxiv.org/abs/2411.17467). The shapes are created using a procedural 3D program that combines primitive shapes (e.g., cubes, spheres, and cylinders) and applies various transformations and augmentations to enhance geometric diversity.
 
 Our dataset is collected based on recent works [Xie et al. (2024)](https://desaixie.github.io/lrm-zero/), and we utilized procedure generated data in self-supervised setting. Each 3D shape is represented by uniformly sampled surface points, making it a versatile resource for pretraining models for tasks such as masked point cloud completion, shape classification, and more.
 
@@ -39,7 +39,7 @@ We evaluated the impact of dataset size on the **PB-T50-RS benchmark** for shape
 
 *Figure 2. The effect of dataset size on downstream shape classification performance. Note that our performance is on par with Point-MAE trained with ShapeNet at exactly the same scale.*
 
-Additional experiments are available in [our paper](https://arxiv.org/abs/xxxx.xxxxx).
+Additional experiments are available in [our paper](https://arxiv.org/abs/2411.17467).
 
 ## Dataset Format
 The dataset is provided in a format ready for point cloud-based learning:
@@ -53,4 +53,13 @@ This dataset is licensed under the [CC BY-SA 4.0 License](https://creativecommon
 If you find this dataset useful in your research, please cite our work:
 
 ```
+@misc{chen2024learning3drepresentationsprocedural,
+      title={Learning 3D Representations from Procedural 3D Programs}, 
+      author={Xuweiyi Chen and Zezhou Cheng},
+      year={2024},
+      eprint={2411.17467},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2411.17467}, 
+}
 ```