Update dataset card with paper link, project links, and sample usage

README.md

@@ -1,27 +1,66 @@
 ---
 license: cc-by-4.0
+size_categories:
+- 1K<n<10K
 task_categories:
-  - object-detection
-tags:
-  - 3d-object-detection
-  - 3d-bounding-box
-  - monocular-3d
-  - in-the-wild
-  - benchmark
+- object-detection
+- zero-shot-object-detection
 pretty_name: WildDet3D Benchmark
-size_categories:
-  - 1K<n<10K
+tags:
+- 3d-object-detection
+- 3d-bounding-box
+- monocular-3d
+- in-the-wild
+- benchmark
 ---
 
 # WildDet3D Benchmark
 
-In-the-wild 3D object detection benchmark (val and test splits) from COCO, LVIS, and Objects365.
+[**Project Page**](https://allenai.github.io/WildDet3D/) | [**Paper**](https://huggingface.co/papers/2604.08626) | [**GitHub**](https://github.com/allenai/WildDet3D)
+
+In-the-wild 3D object detection benchmark (val and test splits) from COCO, LVIS, and Objects365. This benchmark was introduced in the paper [WildDet3D: Scaling Promptable 3D Detection in the Wild](https://huggingface.co/papers/2604.08626).
 
 | Split | Images | Annotations | Categories |
 |-------|--------|-------------|------------|
 | Val | 2,470 | 9,256 | 785 |
 
-**Note:** The test set is held out for hidden evaluation and is not publicly available. Please submit predictions to [TODO: evaluation server] for test set evaluation.
+**Note:** The test set is held out for hidden evaluation and is not publicly available. Please submit predictions to the evaluation server for test set evaluation.
+
+## Sample Usage
+
+You can use the following code snippet from the [official repository](https://github.com/allenai/WildDet3D) to run inference using text prompts with a WildDet3D model:
+
+```python
+from wilddet3d import build_model, preprocess
+from wilddet3d.vis.visualize import draw_3d_boxes
+import numpy as np
+from PIL import Image
+
+# Build model
+model = build_model(
+    checkpoint="ckpt/wilddet3d_alldata_all_prompt_v1.0.pt",
+    score_threshold=0.3,
+    skip_pretrained=True,
+)
+
+# Load and preprocess image
+image = np.array(Image.open("image.jpg")).astype(np.float32)
+
+# With known camera intrinsics
+intrinsics = np.load("intrinsics.npy")  # (3, 3)
+data = preprocess(image, intrinsics)
+
+# Text prompt: detect all instances of given categories
+results = model(
+    images=data["images"].cuda(),
+    intrinsics=data["intrinsics"].cuda()[None],
+    input_hw=[data["input_hw"]],
+    original_hw=[data["original_hw"]],
+    padding=[data["padding"]],
+    input_texts=["car", "person", "bicycle"],
+)
+boxes, boxes3d, scores, scores_2d, scores_3d, class_ids, depth_maps = results
+```
 
 ## Download
 
@@ -75,6 +114,20 @@ Same format as [WildDet3D-Data](https://huggingface.co/datasets/weikaih/WildDet3
 - **`bbox3D_cam`**: 8 corner points of the 3D bounding box
 - **`bbox2D_proj`**: 2D bounding box `[x1, y1, x2, y2]`
 
+## Citation
+
+```bibtex
+@misc{huang2026wilddet3dscalingpromptable3d,
+      title={WildDet3D: Scaling Promptable 3D Detection in the Wild}, 
+      author={Weikai Huang and Jieyu Zhang and Sijun Li and Taoyang Jia and Jiafei Duan and Yunqian Cheng and Jaemin Cho and Mattew Wallingford and Rustin Soraki and Chris Dongjoo Kim and Donovan Clay and Taira Anderson and Winson Han and Ali Farhadi and Bharath Hariharan and Zhongzheng Ren and Ranjay Krishna},
+      year={2026},
+      eprint={2604.08626},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2604.08626}, 
+}
+```
+
 ## License
 
-CC BY 4.0
+CC BY 4.0