Link dataset release to paper and models

README.md

@@ -3,11 +3,15 @@ license: cc-by-4.0
 pretty_name: 3D-CovDiffusion Train-Ready Dataset
 tags:
   - robotics
+  - robot-learning
+  - industrial-robotics
   - 3d
   - point-cloud
   - diffusion-policy
   - trajectory-generation
   - coverage-path-planning
+  - robotic-spray-painting
+  - iros-2026
 size_categories:
   - 1K<n<10K
 ---
@@ -20,6 +24,16 @@ training tensors and self-contained evaluation inputs for the complete fixed
 test splits. Users do not need to rebuild point clouds, trajectories, or mesh
 geometry from raw records.
 
+- Paper: https://arxiv.org/abs/2510.03011
+- Project page: https://crystalccy1.github.io/3D-CovDiffusion/
+- Code: https://github.com/crystalccy1/3D-CovDiffusion
+- Models: https://huggingface.co/ChenyuanC/3D-CovDiffusion
+
+The paper reports a jointly trained, category-agnostic model. The current
+public code-and-model release provides separately trained checkpoints for
+Windows, Cuboids, Shelves, and Containers; this dataset contains the matching
+per-category training and fixed-test records.
+
 ## Contents
 
 | Category | Training episodes | Training steps | Evaluation cases |
@@ -109,6 +123,10 @@ Use `cuboids`, `shelves`, or `containers` for another category.
             and Liu, Tianyu and He, Zewen and Duan, Anqing
             and Nakamura, Yoshihiko},
   year   = {2026},
+  eprint = {2510.03011},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.RO},
+  url    = {https://arxiv.org/abs/2510.03011},
   note   = {Accepted at IROS 2026}
 }
 ```