GridNet-HD Baseline: Late Fusion MLP on Dual Softmax Outputs

Overview

• Per-zone preprocessing of two LiDAR softmax files (image_vote and spt) into combined feature tensors
• A lightweight SimpleMLP model that concatenates the two softmax vectors per point
• Training, validation and inference loops
• Weights & Biases integration for real-time experiment tracking

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

Table of Contents

• Project Structure
• Configuration
• Environment
• Dataset Structure
• Installation
• Supported Modes
• Results
• Pretrained Weights
• Usage Examples
• Weights & Biases Integration
• License
• Contact
• Citation

Project Structure

project_root/
├── main.py # Entry point (all modes)
├── config.yaml # Configuration parameters
├── dataset/
│ ├── lidar_dataset.py # dataset for training/validation/test
│ └── preprocess_multi_processing.py # prepare files for training
├── las_utils/
│ ├── io.py # .las reading
│ └── matching.py # Nearest-neighbor matching
├── model/
│ └── model.py # SimpleMLP definition
├── train/
│ ├── train.py # eval and train loop
│ └── test.py # inference function for test split
├── utils/
│ ├── logging_utils.py
│ └── metrics.py # compute all metrics
├── requirements.txt # Python dependencies
└── README.md # This file

Configuration

Key sections

dataset

• root: path to the folder containing all raw zones
• split_file: path to a JSON file defining train/val/test splits
• n_classes: number of target classes
• voxel_size: downsampling voxel size (used for KDTree)
• pre-processing_num_workers: parallelism for data preprocessing
• max_point_per_class: maximum number of points sampled per class during training
• class_map: label remapping rules (original → new class)

training

• batch_size, epochs, learning_rate, weight_decay
• lr_step_size, lr_gamma: learning rate scheduler (StepLR)
• device: "cuda" or "cpu"

model

• hidden_dims: list of layer widths
• ignore_index: label to ignore during loss computation

logging

• save_dir: where to store logs and model weights
• save_freq: save checkpoint every N epochs

wandb

• project: W&B project name
• entity: your W&B team or username

Environment

Dataset structure

/path/to/data/
├── t1z5b/
│   ├── lidar_softmax_image_vote/t1z4_with_softmax.las   # LiDAR with soft-log from ImageVote baseline
│   ├── lidar_softmax_spt/t1z4_with_softmax.las          # LiDAR with soft-log from SPT baseline
│   └── lidar/t1z4.las                                   # ground-truth
├── …
└── split.json                      # maps zones → train/val/test

/path/to/data/preprocessed/
├── t1z4.pt        # contains {features and labels}
├── t1z5a.pt
└── …

Installation

1. Clone the repository:
   bash

   1git clone https://github.com/heig-vd-geo/baseline_fusion_mlp.git 2cd baseline_fusion_mlp
2. Create a conda virtual environment:
   bash

   1conda create -n gridnet_hd_mlp python=3.12 2conda activate gridnet_hd_mlp 3
3. Install dependencies:
   bash

   1pip install --upgrade pip 2pip install -r requirements.txt

Supported modes

Results

Pretrained Weights

Usage examples

Preprocessing

Training

Validation

Test (Las export)

Weights & Biases Integration

• Login:

• Set logging.wandb.project & .entity in config.yaml.

License

Contact

Citation

@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}, 
}