Enhanced Spatiotemporal Consistency for Image-to-LiDAR Data Pretraining.

Area of Science:

• Computer Vision
• Machine Learning
• Robotics

Background:

• LiDAR representation learning reduces annotation costs.
• Existing methods often neglect temporal dynamics in driving scenarios.
• Spatiotemporal information is crucial for motion and scene continuity.

Purpose of the Study:

• To propose SuperFlow++, a framework integrating spatiotemporal cues for LiDAR representation learning.
• To address the limitations of existing methods in capturing temporal dynamics.
• To establish a new benchmark for data-efficient LiDAR-based perception.

Main Methods:

• Integrating spatiotemporal cues using consecutive LiDAR-camera pairs.
• Employing a view consistency alignment module for unified semantic information.
• Utilizing dense-to-sparse consistency regularization for feature robustness.
• Implementing flow-based contrastive learning for temporal relationship modeling.
• Applying a temporal voting strategy for semantic information propagation.

Main Results:

• SuperFlow++ outperforms state-of-the-art methods on 11 diverse LiDAR datasets.
• The framework demonstrates strong performance across various tasks and driving conditions.
• Scaling 2D and 3D backbones reveals emergent properties for scalable 3D foundation models.

Conclusions:

• SuperFlow++ establishes a new benchmark for data-efficient LiDAR perception in autonomous driving.
• The framework offers strong generalizability and computational efficiency.
• The approach provides insights into developing scalable 3D foundation models.