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DeGuNet: Ultra-Compact Backbone Boosts LiDAR-Camera 3D Detection Efficiency

By PulseAugur Editorial · [4 sources] ·

Researchers have developed DeGuNet, a new ultra-compact image backbone designed to improve the efficiency and accuracy of 3D object detection in autonomous driving systems. This plug-and-play module integrates LiDAR and camera data more effectively by incorporating sparsity-aware feature extraction, which aligns multi-view images with LiDAR depth data while preventing invalid region contamination. Experiments on the nuScenes dataset show that DeGuNet can significantly reduce GPU memory usage by up to 66.5% and speed up inference by 1.16x, while simultaneously improving detection accuracy by up to 6.20 absolute mAP. AI

IMPACT Enhances efficiency and accuracy in autonomous driving perception systems by optimizing multi-modal data fusion.

RANK_REASON Academic paper detailing a new technical approach. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

DeGuNet: Ultra-Compact Backbone Boosts LiDAR-Camera 3D Detection Efficiency

COVERAGE [4]

  1. arXiv cs.CV TIER_1 English(EN) · Haifa Zhang, Yijing Wang, Peixi Peng, Zhiqiang Zuo ·

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

    arXiv:2607.12419v1 Announce Type: new Abstract: In autonomous driving perception, the fusion of LiDAR and camera modalities has become the dominant paradigm for 3D object detection. However, current multi-modal frameworks heavily rely on massive visual backbones pretrained on 2D …

  2. arXiv cs.CV TIER_1 English(EN) · Haojie Ren, Songrui Luo, Lingfeng Wang, Yan Xia, Yao Li, Jing Li, Lu Zhang, Jiajun Deng, Yanyong Zhang ·

    ViCo3D: Empowering LiDAR-based Collaborative 3D Object Detection with Vision Foundation Models

    arXiv:2607.12959v1 Announce Type: new Abstract: LiDAR-based collaborative 3D perception in Vehicle-to-Everything (V2X) systems typically relies on fusing bird's-eye-view (BEV) features across agents. However, current BEV representations, typically extracted by LiDAR backbones tra…

  3. arXiv cs.CV TIER_1 English(EN) · Yanyong Zhang ·

    ViCo3D: Empowering LiDAR-based Collaborative 3D Object Detection with Vision Foundation Models

    LiDAR-based collaborative 3D perception in Vehicle-to-Everything (V2X) systems typically relies on fusing bird's-eye-view (BEV) features across agents. However, current BEV representations, typically extracted by LiDAR backbones trained from scratch, are geometry-dominated and la…

  4. arXiv cs.CV TIER_1 English(EN) · Zhiqiang Zuo ·

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

    In autonomous driving perception, the fusion of LiDAR and camera modalities has become the dominant paradigm for 3D object detection. However, current multi-modal frameworks heavily rely on massive visual backbones pretrained on 2D semantic tasks. This reliance introduces substan…

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