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@@ -27,7 +27,7 @@ The dataset is associated with the following paper:
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  > **Authors**: Masked for instance
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- > **Conference**: Submitted to NeurIPS 2025
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  This repository hosts the official data splits and resources used in the experiments reported in the paper.
@@ -193,23 +193,25 @@ Please follow instructions on dedicated git repository for models running on thi
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  - Baseline based on image segmentation and reprojection into LiDAR: [ImageVote baseline](https://huggingface.co/heig-vd-geo/ImageVote_GridNet-HD_baseline)
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  - Baseline based on LiDAR 3D segmentation directly using Superpoint Trasnformer (SPT): [SPT baseline](https://huggingface.co/heig-vd-geo/SPT_GridNet-HD_baseline)
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  - Baseline based on late fusion between softmax logits from SPT and ImageVote: [LateFusionMLP baseline](https://huggingface.co/heig-vd-geo/LateFusionMLP_GridNet-HD_baseline)
 
 
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  Results are visible here with the **best model** from 3 different baselines:
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- | Baseline | ImageVote baseline | SPT baseline| Late fusion MLP|
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- |---------------------------|-----------------------|---------------|------------------|
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- | Class | IoU (Test set) (%)| IoU (Test set) (%)| IoU (Test set) (%)|
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- | Pylon | 85.09 | 92.75 | **94.82** |
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- | Conductor cable | 64.82 | 91.05 | **94.40** |
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- | Structural cable | 45.06 | 70.51 | **82.52** |
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- | Insulator | 71.07 | 80.60 | **86.98** |
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- | High vegetation | 83.86 | **85.15** | 83.08 |
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- | Low vegetation | **63.43** | 55.91 | 47.64 |
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- | Herbaceous vegetation | 84.45 | **84.64** | 80.75 |
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- | Rock, gravel, soil | 38.62 | 40.63 | **42.89** |
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- | Impervious soil (Road) | **80.69** | 73.57 | 80.26 |
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- | Water | **74.87** | 3.69 | 61.69 |
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- | Building | **68.09** | 57.38 | 61.40 |
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- | **Mean IoU (mIoU)** | 69.10 | 66.90 | **74.22** |
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  ---
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  GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure
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  Masked Authors
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- Submitted to NeurIPS 2025.
 
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  > **Authors**: Masked for instance
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+ > **Conference**: Submitted to CVPR 2026
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  This repository hosts the official data splits and resources used in the experiments reported in the paper.
 
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  - Baseline based on image segmentation and reprojection into LiDAR: [ImageVote baseline](https://huggingface.co/heig-vd-geo/ImageVote_GridNet-HD_baseline)
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  - Baseline based on LiDAR 3D segmentation directly using Superpoint Trasnformer (SPT): [SPT baseline](https://huggingface.co/heig-vd-geo/SPT_GridNet-HD_baseline)
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  - Baseline based on late fusion between softmax logits from SPT and ImageVote: [LateFusionMLP baseline](https://huggingface.co/heig-vd-geo/LateFusionMLP_GridNet-HD_baseline)
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+ - Baseline based on the recent PTv3 model: [PTv3 baseline](https://huggingface.co/heig-vd-geo/PTv3_GrdiNet-HD_baseline)
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+ - Baseline based on the current SOTA of 3D/2D fusion, DINO In The Room (DITR): [DITR baseline](https://huggingface.co/heig-vd-geo/PTv3_GrdiNet-HD_baseline)
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  Results are visible here with the **best model** from 3 different baselines:
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+ | Baseline | ImageVote baseline | SPT baseline| Late fusion MLP| PTv3 IoU (%) | DITR IoU (%) |
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+ |---------------------------|-----------------------|---------------|------------------|---------------|------------------|
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+ | Class | IoU (Test set) (%)| IoU (Test set) (%)| IoU (Test set) (%)|IoU (Test set) (%)| IoU (Test set) (%)|
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+ | Pylon | 85.09 | 92.75 | 94.82 |97.12 | 96.81 |
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+ | Conductor cable | 64.82 | 91.05 | 94.40 |85.88 | 89.07 |
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+ | Structural cable | 45.06 | 70.51 | 82.52 |53.22 | 57.80 |
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+ | Insulator | 71.07 | 80.60 | 86.98 |90.63 | 93.20 |
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+ | High vegetation | 83.86 | 85.15 | 83.08 |88.30 | 88.81 |
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+ | Low vegetation | 63.43 | 55.91 | 47.64 |33.93 | 41.99 |
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+ | Herbaceous vegetation | 84.45 | 84.64 | 80.75 |91.72 | 90.05 |
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+ | Rock, gravel, soil | 38.62 | 40.63 | 42.89 | 51.88 | 44.26 |
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+ | Impervious soil (Road) | 80.69 | 73.57 | 80.26 |79.63 | 79.49 |
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+ | Water | 74.87 | 3.69 | 61.69 |29.68 | 71.86 |
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+ | Building | 68.09 | 57.38 | 61.40 |60.49 | 70.26 |
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+ | **Mean IoU (mIoU)** | **69.10** | **66.90** | **74.22** |**69.32** | **74.87** |
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  ---
 
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  GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure
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  Masked Authors
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+ Submitted to CVPR 2026.