Improve dataset card: Add task category, paper link, and Github repo link

README.md

@@ -1,15 +1,19 @@
 ---
 license: cc-by-nc-4.0
+task_categories:
+- image-to-3d
 configs:
-  - config_name: default
-    data_files:
-      - split: test
-        path: "test.csv"
+- config_name: default
+  data_files:
+  - split: test
+    path: test.csv
 ---
 
 # SpatialLM Testset
 
-We provide a test set of 107 preprocessed point clouds and their corresponding GT layouts, point clouds are reconstructed from RGB videos using [MASt3R-SLAM](https://github.com/rmurai0610/MASt3R-SLAM). SpatialLM-Testset is quite challenging compared to prior clean RGBD scan datasets due to the noises and occlusions in the point clouds reconstructed from monocular RGB videos.
+[Project page](https://manycore-research.github.io/SpatialLM) | [Paper](https://arxiv.org/abs/2506.07491) | [Code](https://github.com/manycore-research/SpatialLM)
+
+We provide a test set of 107 preprocessed point clouds and their corresponding GT layouts. Point clouds are reconstructed from RGB videos using [MASt3R-SLAM](https://github.com/rmurai0610/MASt3R-SLAM). SpatialLM-Testset is quite challenging compared to prior clean RGBD scan datasets due to the noises and occlusions in the point clouds reconstructed from monocular RGB videos.
 
 <table style="table-layout: fixed;">
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@@ -57,4 +61,4 @@ Use `rerun` to visualize the point cloud and the GT structured 3D layout output:
 ```bash
 python visualize.py --point_cloud pcd/scene0000_00.ply --layout layout/scene0000_00.txt --save scene0000_00.rrd
 rerun scene0000_00.rrd
-```
+```