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Published on: May 7, 2019
LapDINO: A DINOv3 and Laplacian Pyramid-Based Approach for Outdoor Terrain Segmentation
Shiquan Ling1,2, Xingchen Qin2, Wenkang Xu2
1School of Mechanical Engineering, Zhejiang University, Hangzhou 310030, China.
Sensors (Basel, Switzerland)
|June 26, 2026
Summary
LapDINO enhances autonomous driving by integrating semantic understanding from DINOv3 with Laplacian pyramid details for precise off-road terrain segmentation. This method improves safety in complex environments.
Area of Science:
- Computer Vision
- Robotics
- Artificial Intelligence
Background:
- Autonomous driving requires accurate terrain understanding for safe navigation, especially in unstructured outdoor environments.
- Traditional supervised learning struggles with outdoor complexities like variable lighting and high costs of pixel-level annotation.
Purpose of the Study:
- To develop a novel method for robust and efficient off-road terrain segmentation.
- To overcome limitations of supervised learning in dynamic and complex outdoor environments.
Main Methods:
- Proposed LapDINO, a dual-path bidirectional interactive encoder combining DINOv3's semantic features with Laplacian pyramid's multi-scale frequency analysis.
- Implemented a bidirectional cross-attention fusion mechanism for semantic and geometric detail interaction.
- Introduced lightweight visual adapters for efficient fine-tuning of DINOv3 to address domain shift.
Main Results:
- Achieved state-of-the-art performance in off-road terrain segmentation.
- Demonstrated an optimal balance between accuracy and computational efficiency.
- Successfully constructed two new datasets (VOTD and VOCD) for off-road terrain segmentation research.
Conclusions:
- LapDINO offers a robust and efficient engineering solution for terrain perception in autonomous driving.
- The method effectively combines semantic and structural information for improved navigation safety.
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