Add link to paper

This PR ensures the dataset is linked to (and can be found at) https://huggingface.co/papers/2609.07414.

README.md

@@ -1,6 +1,12 @@
 ---
 language:
 - en
+license: cc-by-4.0
+size_categories:
+- 10M<n<100M
+task_categories:
+- image-to-image
+- depth-estimation
 tags:
 - relighting
 - multi-view
@@ -9,12 +15,6 @@ tags:
 - hdr
 - rendering
 - depth-maps
-task_categories:
-- image-to-image
-- depth-estimation
-license: cc-by-4.0
-size_categories:
-- 10M<n<100M
 ---
 
 <p align="center">
@@ -56,7 +56,7 @@ size_categories:
 
 ## 📖 Dataset Summary
 
-The **Laval Objaverse Dataset** is a comprehensive dataset designed for multi-view relighting and novel view synthesis tasks. It combines high-quality 3D assets from Objaverse with realistic, diverse illumination conditions from the Laval Indoor and Outdoor HDR datasets. Each render includes synchronized multi-view images, depth maps, and complete lighting metadata.
+The **Laval Objaverse Dataset** is a comprehensive dataset introduced in the paper [RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting](https://huggingface.co/papers/2609.07414), designed for multi-view relighting and novel view synthesis tasks. It combines high-quality 3D assets from Objaverse with realistic, diverse illumination conditions from the Laval Indoor and Outdoor HDR datasets. Each render includes synchronized multi-view images, depth maps, and complete lighting metadata.
 
 ## 🏗️ Dataset Construction