Voxel to Pillar Distillation

Voxel-to-pillar distillation is a knowledge distillation technique focusing on transferring knowledge from computationally expensive 3D voxel-based models to more efficient Bird's-Eye-View (BEV) models for tasks like LiDAR semantic segmentation and object detection. Current research emphasizes distilling various forms of knowledge, including instance-level features, spatial relations (both local and global), and even simulating multi-modality data (e.g., fusing LiDAR and image data). This approach aims to improve the speed and efficiency of these models without sacrificing significant accuracy, leading to advancements in autonomous driving and other applications requiring real-time 3D scene understanding.