DeGuNet: Depth-Guided Ultra-Compact Backbones for Efficient LiDAR-Camera 3D Detection

Jul 15, 2026

A new preprint introduces DeGuNet, an ultra-compact image backbone designed for LiDAR-camera 3D detection in autonomous driving. DeGuNet uses depth-guided representation learning to address parameter redundancy in current multi-modal frameworks, reducing GPU memory usage by up to 66.5% and achieving a 1.16x speedup. The method also improves mean average precision (mAP) by up to 6.20 points on the nuScenes dataset, demonstrating both efficiency and accuracy gains.

Why it matters: DeGuNet could enable more efficient and accurate 3D perception for autonomous vehicles by significantly reducing computational demands.

Full story at: arXiv Computer Vision ↗