Add dataset illustration triptych to README

Update the dataset card to show raw_3d, 3d_segmented, and corresponding_image on one line.

README.md

@@ -23,19 +23,21 @@ This dataset is associated with a public leaderboard hosted on Hugging Face Spac
 
 The dataset is associated with the following paper:
 
-```
-@misc{gridnet-hd-dataset,
-      title={GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure}, 
-      author={Antoine Carreaud and Shanci Li and Malo De Lacour and Digre Frinde and Jan Skaloud and Adrien Gressin},
-      year={2026},
-      eprint={2601.13052},
-      url={https://arxiv.org/abs/2601.13052}, 
-}
-```
+> **Title**: GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure
+
+> **Authors**: Masked for instance
+
+> **Conference**: Submitted to NeurIPS 2025 
 
 
 This repository hosts the official data splits and resources used in the experiments reported in the paper.
 
+<p align="center">
+  <img src="figures/raw_3d.png" alt="Raw 3D point cloud" width="32%" />
+  <img src="figures/3d_segmented.png" alt="Segmented 3D point cloud" width="32%" />
+  <img src="figures/corresponding_image.png" alt="Corresponding RGB image" width="32%" />
+</p>
+
 ---
 
 ## 2. Dataset Structure
@@ -197,25 +199,23 @@ Please follow instructions on dedicated git repository for models running on thi
 - Baseline based on image segmentation and reprojection into LiDAR: [ImageVote baseline](https://huggingface.co/heig-vd-geo/ImageVote_GridNet-HD_baseline)
 - Baseline based on LiDAR 3D segmentation directly using Superpoint Trasnformer (SPT): [SPT baseline](https://huggingface.co/heig-vd-geo/SPT_GridNet-HD_baseline)
 - Baseline based on late fusion between softmax logits from SPT and ImageVote: [LateFusionMLP baseline](https://huggingface.co/heig-vd-geo/LateFusionMLP_GridNet-HD_baseline)
-- Baseline based on the recent PTv3 model: [PTv3 baseline](https://huggingface.co/heig-vd-geo/PTv3_GridNet-HD_baseline)
-- Baseline based on the current SOTA of 3D/2D fusion, DINO In The Room (DITR): [DITR baseline](https://huggingface.co/heig-vd-geo/PTv3_GridNet-HD_baseline)
 
 Results are visible here with the **best model** from 3 different baselines:
-|               Baseline    |    ImageVote baseline |   SPT baseline|   Late fusion MLP|  PTv3 IoU (%)   | DITR IoU (%)   |
-|---------------------------|-----------------------|---------------|------------------|---------------|------------------|
-| Class                     | IoU (Test set) (%)| IoU (Test set) (%)| IoU (Test set) (%)|IoU (Test set) (%)| IoU (Test set) (%)|
-| Pylon                     |   85.09     |   92.75     |     94.82             |97.12           | 96.81          |
-| Conductor cable           |   64.82     |   91.05     |      94.40            |85.88           | 89.07          |
-| Structural cable          |   45.06     |   70.51     |      82.52           |53.22           | 57.80          |
-| Insulator                 |   71.07     |   80.60     |       86.98            |90.63           | 93.20          |
-| High vegetation           |   83.86     |   85.15     |        83.08          |88.30           | 88.81          |
-| Low vegetation            |   63.43     |   55.91     |        47.64            |33.93           | 41.99          |
-| Herbaceous vegetation     |   84.45     |   84.64     |        80.75        |91.72           | 90.05          |
-| Rock, gravel, soil        |   38.62     |   40.63     |        42.89            | 51.88           | 44.26          |
-| Impervious soil (Road)    |   80.69     |   73.57     |        80.26           |79.63           | 79.49          |
-| Water                     |   74.87     |   3.69      |         61.69          |29.68           | 71.86          |
-| Building                  |   68.09     |   57.38     |         61.40         |60.49           | 70.26          |
-| **Mean IoU (mIoU)**       | **69.10**   |   **66.90** |         **74.22**          |**69.32**       | **74.87**      |
+|               Baseline    |    ImageVote baseline |   SPT baseline|   Late fusion MLP|
+|---------------------------|-----------------------|---------------|------------------|
+| Class                     | IoU (Test set) (%)| IoU (Test set) (%)| IoU (Test set) (%)|
+| Pylon                     |   85.09     |   92.75     |     **94.82**             |
+| Conductor cable           |   64.82     |   91.05     |      **94.40**            |
+| Structural cable          |   45.06     |   70.51     |      **82.52**           |
+| Insulator                 |   71.07     |   80.60     |       **86.98**            |
+| High vegetation           |   83.86     |   **85.15**     |        83.08          |
+| Low vegetation            |   **63.43**     |   55.91     |        47.64            |
+| Herbaceous vegetation     |   84.45     |   **84.64**     |        80.75        |
+| Rock, gravel, soil        |   38.62     |   40.63     |        **42.89**            |
+| Impervious soil (Road)    |   **80.69**     |   73.57     |        80.26           |
+| Water                     |   **74.87**     |   3.69      |         61.69          |
+| Building                  |   **68.09**     |   57.38     |         61.40         |
+| **Mean IoU (mIoU)**       | 69.10   |   66.90 |         **74.22**          |
 
 
 ---
@@ -226,12 +226,6 @@ This dataset is released under the CC-BY-4.0 license.
 
 If you use this dataset, please cite the following paper:
 
-```
-@misc{gridnet-hd-dataset,
-      title={GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure}, 
-      author={Antoine Carreaud and Shanci Li and Malo De Lacour and Digre Frinde and Jan Skaloud and Adrien Gressin},
-      year={2026},
-      eprint={2601.13052},
-      url={https://arxiv.org/abs/2601.13052}, 
-}
-```
+    GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure
+    Masked Authors
+    Submitted to NeurIPS 2025.