Gridnethd Eval

gridnethd-eval

GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure — Carreaud et al. (2026) (arXiv:2601.13052, 2026)

What this evaluates

Evaluates 3D semantic segmentation capabilities for power line infrastructure using multi-modal LiDAR and image data. It probes a model's ability to accurately classify geometric and visual features into 11 distinct classes, including critical assets like pylons, cables, and insulators.

Datasets

• GridNet-HD — total 7694; splits: train (-1), val (-1), test (-1)

Metrics

• mIoU (primary) — range: percent
  • Mean Intersection over Union computed across 11 semantic classes. IoU for a class is calculated as the number of correctly predicted pixels or points divided by the union of predicted and ground truth pixels or points for that class.

Input / output format

Input: Co-georeferenced high-resolution LiDAR point clouds and corresponding images for power line infrastructure scenes.

Output: Per-point or per-voxel semantic class labels from a predefined 11-class ontology (e.g., Pylon, Conductor cable, Insulator, vegetation types, etc.).

Scoring recipe

def compute_miou(preds, gold, num_classes=11):
    ious = []
    for c in range(num_classes):
        tp = np.sum((preds == c) & (gold == c))
        fp = np.sum((preds == c) & (gold != c))
        fn = np.sum((preds != c) & (gold == c))
        iou = tp / (tp + fp + fn) if (tp + fp + fn) > 0 else 0.0
        ious.append(iou)
    return np.mean(ious) * 100

Common pitfalls

• Models may report best single-run performance instead of averaging over 3 training runs with standard deviations.
• Test-Time Augmentation (TTA) and overlap strategies are applied to some baselines (PTv3, DITR) but not others, creating an unfair comparison if not explicitly noted.
• Class imbalance heavily impacts scores, with classes like 'Water' and 'Structural cable' showing drastically lower IoU than dominant classes like 'Pylon' or 'High vegetation'.

Evidence (verbatim from paper)

We report here the detailed per-class IoU scores on the test set for all baselines used in our study. Table 9 shows the average results over 3 training runs, including standard deviations for ImageVote, SPT, and Late Fusion. Table 10 reports the performance of the best model (highest mIoU) selected for each method.

Citation

@misc{carreaud2026gridnethd,
  title={GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure},
  author={Carreaud et al. (2026)},
  year={2026},
  note={arXiv:2601.13052}
}

• arXiv: 2601.13052