GridNet-HD Baseline: Super Point Transformer segmentation

Overview

This repository is a fork of the SuperPoint Transformer (SPT) proposed by Robert et al., adapted to support training and inference on the GridNet-HD dataset.

Our version introduces the following modifications:

Integration with the GridNet-HD dataset structure.
Support for exporting softmax logits per class and per point, used as inputs for the third fusion baseline in our NeurIPS submission.
Support for full resolution inference.

This implementation serves as one of the official baselines provided for GridNet-HD.

For more information on the original SPT architecture, training options, model internals and hyperparameters, please refer to the official SuperPoint Transformer repository.

Configuration
Environment
Dataset Structure
Installation
Supported Modes
Results
Pretrained Weights
Usage Examples
License
Contact
Citation

Configuration

The modified configuration files for training on GridNet-HD are available in config/datamodule/semantic and config/experiment/semantic directory. These include:

dataset paths compatible with GridNet-HD structure,
number of classes,
class mappings.

For all other training parameters (optimizer, scheduler, etc.), we use the defaults from the original SPT repo.

Environment

The following environment was used to train and evaluate the baseline model (detailed requirements are provided by the spt repo).

Component	Details
GPU	NVIDIA A40 (48 GB VRAM)
CUDA Version	12.x
OS	Ubuntu 22.04 LTS
RAM	256 GB

Dataset Structure

The GridNet-HD dataset dataset must be structured as follows for compatibility with this implementation:

project_root/
├── data/
│   └── raw/
│       ├── train/
│       │   ├── t1z4/
│       │   │   └── lidar/
│       │   │       └── t1z4.las
│       │   ├── t2z5/
│       │   │   └── lidar/
│       │   │       └── t2z5.las
│       │   └── ...
│       ├── val/
│       │   ├── t1z5b/
│       │   │   └── lidar/
│       │   │       └── t1z5b.las
│       │   └── ...
│       └── test/
│           ├── t1z4/
│           │   └── lidar/
│           │       └── t1z4.las
│           └── ...

Setup & Installation

Clone the repository:

git clone https://huggingface.co/heig-vd-geo/SPT_GridNet-HD_baseline
cd SPT_GridNet-HD_baseline

Simply run the installation script to set up the required environment (given by the spt repo):

bash install.sh

Supported Modes

The following modes are supported in this fork:

Training on GridNet-HD dataset (with official train.py from spt repo)
Validation using the same split logic (with officiel eval.py from spt.py)
Inference on selected split (with inference.py)
Exporting per-point softmax logits (with inference.py)

For additional informations, refer to the original SPT documentation.

Results

The following table summarizes the per-class Intersection over Union (IoU) scores on the test set at 3D level for the best model.

Class	IoU (Test set) (%)
Pylon	92.75
Conductor cable	91.05
Structural cable	70.51
Insulator	80.60
High vegetation	85.15
Low vegetation	55.91
Herbaceous vegetation	84.64
Rock, gravel, soil	40.63
Impervious soil (Road)	73.57
Water	3.69
Building	57.38
Mean IoU (mIoU)	66.90

Pretrained Weights

🔗 Pretrained weights for the best performing model are available for download directly in this repo.

Usage Examples

Train on GridNet-HD:

python src/train.py experiment=semantic/gridnet

Evaluate on GridNet-HD:

python src/eval.py experiment=semantic/gridnet  ckpt_path=/path/to/your/checkpoint.ckpt

Inference to obtain full-resolution predictions on test point clouds:

python inference.py --mode inference --split test --weights path/to/model.ckpt --root_dir /path/to/data/gridnet/raw

Export Softmax Logits for all splits:

python inference.py --mode export_log --weights path/to/model.ckpt --root_dir /path/to/data/gridnet/raw

This will export .las files with added sof_log0, sof_log1, ..., sof_logN fields representing softmax scores per class and per point to train the 3rd baseline.

License

This project is open-sourced under the MIT License. The original SuperPoint Transformer repository is licensed under the same MIT Licence.

Contact

For questions, issues, or contributions, please open an issue on the repository.

Citation

If you use this repo in research, please cite:

GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure
Masked Authors
Submitted to NeurIPS 2025.

heig-vd-geo
/

SPT_GridNet-HD_baseline