Datasets:
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README.md
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> **Authors**: Masked for instance
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> **Conference**: Submitted to
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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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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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| Class | IoU (Test set) (%)| IoU (Test set) (%)| IoU (Test set) (%)|
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| Pylon | 85.09 | 92.75 |
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| Conductor cable | 64.82 | 91.05 |
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| Structural cable | 45.06 | 70.51 |
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| Insulator | 71.07 | 80.60 |
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| High vegetation | 83.86 |
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| Low vegetation |
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| Herbaceous vegetation | 84.45 |
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| Rock, gravel, soil | 38.62 | 40.63 |
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| Impervious soil (Road) |
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| Water |
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| Building |
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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
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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.
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