Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving.

Area of Science:

• Computer Vision
• Robotics
• Machine Learning

Background:

• Fully supervised methods for 3D scene understanding in autonomous driving are challenged by the high cost of human-annotated LiDAR point clouds.
• Efficient data utilization is critical for advancing autonomous driving perception systems.

Purpose of the Study:

• To develop a semi-supervised learning framework for LiDAR semantic segmentation that leverages unlabeled data effectively.
• To introduce LaserMix++, a novel approach for data-efficient learning in 3D scene understanding.

Main Methods:

• LaserMix++ integrates laser beam manipulations from disparate LiDAR scans and LiDAR-camera correspondences.
• The framework employs multi-modal strategies: multi-modal LaserMix, camera-to-LiDAR feature distillation, and language-driven knowledge guidance.
• It enhances 3D scene consistency regularization through cross-sensor interactions and auxiliary supervisions.

Main Results:

• LaserMix++ achieves comparable accuracy to fully supervised methods with five times fewer annotations.
• The framework significantly outperforms supervised-only baselines in LiDAR semantic segmentation.
• Demonstrates substantial improvement in data-efficient learning for 3D scene understanding.

Conclusions:

• Semi-supervised learning, as implemented by LaserMix++, offers a powerful solution to reduce reliance on extensive labeled data in autonomous driving.
• The proposed framework is versatile, applicable across various LiDAR representations, and establishes a new standard for efficient 3D scene understanding.