LiDAR Distillation

LiDAR distillation focuses on transferring knowledge from complex, data-hungry LiDAR models to more efficient, lightweight alternatives, thereby improving the performance and resource efficiency of 3D perception systems. Current research emphasizes teacher-student architectures, often incorporating contrastive learning or adversarial training methods, and explores multi-modal fusion with camera data to enhance feature representation learning. This technique is significant for autonomous driving and robotics, promising to reduce the reliance on extensive labeled LiDAR data and enable deployment on resource-constrained platforms while maintaining high accuracy in 3D scene understanding.