Knowledge Distillation: A Review of Methods and Applications in LiDAR Data
DOI:
https://doi.org/10.26438/ijcse/v13i11.7589Keywords:
Knowledge distillation, LiDAR, Autonomous Systems, Object Detection, Semantic Segmentation, Point CloudsAbstract
Knowledge distillation (KD) is a machine learning technique where a compact student model is trained by a larger teacher model to create efficient, high-performance models suitable for devices with limited computational resources. The student learns by mimicking the teacher’s nuanced predictions, known as ”soft targets”, which provide a richer learning signal than traditional ground-truth labels. Methods are categorized by the source of knowledge—such as the teacher’s final outputs (response-based), intermediate features (feature-based), or the relationships between data points (relation-based)—and by the training strategy, including offline, online, and self-distillation schemes. This review focuses on the application of KD to 2D and 3D LiDAR data for tasks like object detection and semantic segmentation in autonomous systems. In this domain, knowledge distillation is critical for developing lightweight models that can run in real-time, enabling cross-modal learning from expensive LiDAR to cheaper sensors, and addressing inherent challenges of point cloud data such as sparsity and sensor-specific domain gaps.
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