HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-trainin
arXiv:2606.20189v1 Announce Type: cross Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD). However, current approaches typically treat VFMs as black-box teachers, relying exclusively on frame-wise feature similarity. Consequently, they do not fully exploit the teacher's layer-wise semantic structure and global context, as well as the rich spatiotemporal information inherent in LiDAR se
The continuous drive towards more robust and efficient perception systems for autonomous driving, coupled with advances in diffusion models and self-supervised learning, makes this a natural progression in AI research.
This research addresses a critical bottleneck in autonomous driving development – the scarcity of annotated LiDAR data – by improving knowledge transfer from rich vision models, potentially accelerating AD system capabilities.
The proposed HilDA method enhances LiDAR pre-training by fully exploiting the semantic and spatiotemporal information from Vision Foundation Models, leading to more capable and data-efficient perception systems.
- · Autonomous Driving Companies
- · LiDAR Manufacturers
- · AI Perception Developers
- · Robotics Industries
- · Companies reliant on extensive manual LiDAR annotation
- · Traditional LiDAR perception methods
More accurate and robust LiDAR perception systems for autonomous vehicles become available.
Accelerated development and deployment of L3+ autonomous driving functionalities due to improved sensor fusion and understanding.
Enhanced safety and reliability of autonomous systems, potentially expanding their application beyond road vehicles to other robotic domains.
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Read at arXiv cs.AI