Improve dataset card: Add task categories, tags, paper, code and project page links, and sample usage (#1)

- Improve dataset card: Add task categories, tags, paper, code and project page links, and sample usage (10da239a9e3c925f6359e407a342b65798ccee6c)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,7 +1,24 @@
 ---
 license: mit
+task_categories:
+- image-to-3d
+- image-segmentation
+- text-retrieval
+tags:
+- 3d-reconstruction
+- semantic-segmentation
+- instance-segmentation
+- panoptic-segmentation
+- referring-segmentation
+- scannet
+- english
 ---
-This is official huggingface repository for [SIU3R](https://arxiv.org/abs/2507.02705)
+
+This is the official Hugging Face repository for [SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment](https://huggingface.co/papers/2507.02705).
+
+Project Page: https://insomniaaac.github.io/siu3r/
+Code: https://github.com/WU-CVGL/SIU3R
+
 # Pretrained Models for SIU3R
 We provide pretrained models for the Panoptic Segmentation task. We train MASt3R backbone with adapter on the COCO dataset for SIU3R initialization.
 
@@ -135,7 +152,7 @@ For refer segmentation task, we provide the refer segmentation annotations in tr
                 [49406, 589, 533, 320, 1538, 2175, 269, 997, 631, 2097, 2866, 12033, 2403, 585, 269, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                 [49406, 589, 533, 320, 1538, 2175, 269, 585, 533, 13589, 638, 12033, 269, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                 [49406, 997, 533, 320, 3638, 2175, 530, 518, 1530, 269, 585, 791, 2581, 12033, 8525, 705, 531, 585, 269, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-                [49406, 320, 2866, 2175, 267, 9729, 530, 518, 3694, 539, 518, 1530, 267, 525, 518, 1823, 533, 275, 2866, 12033, 267, 525, 518, 1155, 631, 275, 2866, 12033, 269, 518, 2184, 533, 320, 2866, 2489, 593, 1395, 10485, 525, 269, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                [49406, 320, 2866, 2175, 267, 9729, 530, 518, 3694, 539, 518, 1530, 267, 525, 518, 1823, 530, 518, 5407, 539, 1093, 269, 518, 1155, 631, 275, 2866, 12033, 269, 518, 2184, 533, 320, 2866, 2489, 593, 1395, 10485, 525, 269, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                 [49406, 589, 533, 320, 2866, 2175, 269, 585, 533, 13589, 638, 4135, 320, 1939, 11840, 12033, 269, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
             ]
         },
@@ -146,6 +163,20 @@ For refer segmentation task, we provide the refer segmentation annotations in tr
 ```
 The "scene0011_00" field is the scan name, the "2" field is the object id (also instance_label), the "object_name" field is the object name, the "instance_label_id" field is the semantic label id in instance segmentation task, the "panoptic_label_id" field is the semantic label id in panoptic segmentation task, the "frame_id" field is the frame ids of images which contain this object, the "text" field is the refer segmentation text description, and the "text_token" field is the tokenized refer segmentation text by openclip (https://github.com/mlfoundations/open_clip), note that we use `convnext_large_d_320` model (https://huggingface.co/laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup). The refer segmentation task is to segment the object in the image based on the refer segmentation text. This part of data is obtained from the uniseg3d repository (https://github.com/dk-liang/UniSeg3D), thanks for their great work.
 
+## Sample Usage
+To run inference with the SIU3R model using this dataset, you first need to download the pre-trained model checkpoint and place it in the `pretrained_weights` directory (as described in the [GitHub repository](https://github.com/WU-CVGL/SIU3R)).
+
+Then, you can run the inference script:
+```bash
+python inference.py --image_path1 <path_to_image1> --image_path2 <path_to_image2> --output_path <output_directory> [--cx <cx_value>] [--cy <cy_value>] [--fx <fx_value>] [--fy <fy_value>]
+```
+A `output.ply` will be generated in the specified output directory, containing the reconstructed gaussian splattings. The `cx`, `cy`, `fx`, and `fy` parameters are optional and can be used to specify the camera intrinsics. If not provided, default values will be used.
+
+You can view the results in the online viewer by running:
+```bash
+python viewer.py --output_ply <output_directory/output.ply>
+```
+
 # Citation
 If you find our work useful, please consider citing our paper:
 ```bibtex