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lidar-segmentation-beyond-road
roboticslaburjc-org/lidar-segmentation-beyond-road
lidar-segmentation-beyond-road is a machine learning model from roboticslaburjc-org. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-nc-4.0.
This repository provides model weights and evaluation dataset for the paper LiDAR Segmentation Beyond the Road: Performance and Robustness in Off-Road Settings:
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Updated Feb 18, 2026
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At a glance
- License
- cc-by-nc-4.0
- Access
- Public
- Created
- Feb 18, 2026
- Updated
- Feb 18, 2026
- SHA
- 14eeee12
Model card
From the Hugging Face model README
LiDAR Segmentation Beyond the Road: Performance and Robustness in Off-Road Settings
David Pascual-Hernández, Roberto Calvo-Palomino, Inmaculada Mora-Jiménez, Jose María Cañas-Plaza
This repository provides model weights and evaluation dataset for the paper LiDAR Segmentation Beyond the Road: Performance and Robustness in Off-Road Settings:
dataset/: labeled LiDAR scans from proprietary sensor data provided by Celestia|TST.weights/: trained model weights for all experiments in the paper.
Weights naming
<dataset>-<model>-<input_features>-<data_augmentation>
- Dataset: training dataset used.
- Model: KPConv, RandLANet, MinkUNet, SPVCNN, Cylinder3D, SphereFormer, or LSK3DNet.
- Input features: point features used as input (
xyzorxyziif intensity is included). - Data augmentation: none, light or strong.
Models have been finetuned from the pretrained weights provided by:
- Open3D-ML → KPConv, RandLANet
- mmdetection3d → MinkUNet, SPVCNN, Cylinder3D
- Official repositories → SphereFormer, LSK3DNet
- License
- cc-by-nc-4.0
- Created
- Feb 18, 2026
- Updated
- Feb 18, 2026