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Related Experiment Video

Updated: Mar 15, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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MS2-CL: Multi-Scale Self-Supervised Learning for Camera to LiDAR Cross-Modal Place Recognition.

Wen Liu1, Lei Ma1, Xuanshun Zhuang1

  • 1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
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This study introduces a novel method for cross-modal place recognition, enabling robots and autonomous vehicles to localize using both visual and 3D point cloud data. The approach achieves state-of-the-art performance by learning a unified embedding space, overcoming domain gaps and improving generalization.

Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Machine Learning

Background:

  • Place recognition is crucial for autonomous navigation, but cross-modal localization (e.g., visual to 3D point clouds) faces significant challenges.
  • Existing methods struggle with domain gaps, computational costs, and learning viewpoint/scale-invariant features.

Purpose of the Study:

  • To develop a robust cross-modal place recognition framework that addresses the limitations of current approaches.
  • To enable accurate visual localization within large-scale 3D point cloud maps.

Main Methods:

  • Formulated cross-modal recognition as learning a scale-invariant, unified embedding space.
  • Employed a hierarchical Swin Transformer for multi-scale feature extraction from unified 2D representations.
Keywords:
Swin Transformerautonomous drivingcross-modal place recognitionself-supervised learning

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  • Utilized a multi-scale self-distillation paradigm for intra-modal knowledge transfer.
  • Achieved inter-modal alignment using a global contrastive loss on 'teacher' embeddings.

    • Main Results:

    • Achieved state-of-the-art performance on KITTI and KITTI-360 datasets.
    • Demonstrated high accuracy in visual localization within 3D point cloud maps.
    • Achieved over 60% Recall@1 on KITTI-360 without fine-tuning, using a KITTI-trained model.

    Conclusions:

    • The proposed method effectively bridges the domain gap between visual and 3D point cloud data for place recognition.
    • The scale-invariant unified embedding space and self-distillation approach enhance generalization and performance.
    • The framework offers a promising solution for reliable cross-modal localization in robotics and autonomous vehicles.