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Found 3 papers, 2 papers with code
Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection
In this paper, we propose a framework named the Adaptive Distance Interval Separation Network (ADISN) that adopts a novel perspective on understanding depth maps, as a form that lies between LiDAR and images.
Multi-to-Single Knowledge Distillation for Point Cloud Semantic Segmentation
In this paper, we propose a novel multi-to-single knowledge distillation framework for the 3D point cloud semantic segmentation task to boost the performance of those hard classes.
Knowledge Distillation from 3D to Bird's-Eye-View for LiDAR Semantic Segmentation
However, it is difficult for existing models to achieve both high inference speed and accuracy simultaneously.
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