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 lack general semantic priors, inherently limiting the efficacy of feature-level collaboration. Meanwhile, vision foundation models (VFMs) pretrained on large-scale image data have demonstrated strong capability in learning general-purpose and informative visual representations for 2D tasks, and have the potential to enha
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- PossiblePossibly related (embedding) · 51%Into the Omniverse: Three Workflows for Improving Vision AI Agent Accuracy With Synthetic Data and Fine-Tuning →
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- LinkedLinked via arxiv author · 85%Haojie Ren →
“ViCo3D: Empowering LiDAR-based Collaborative 3D Object Detection with Vision Foundation Models”
- LinkedLinked via arxiv author · 85%Songrui Luo →
“ViCo3D: Empowering LiDAR-based Collaborative 3D Object Detection with Vision Foundation Models”
- LinkedLinked via arxiv author · 85%Lingfeng Wang →
“ViCo3D: Empowering LiDAR-based Collaborative 3D Object Detection with Vision Foundation Models”
- LinkedLinked via arxiv author · 85%Yan Xia →
“ViCo3D: Empowering LiDAR-based Collaborative 3D Object Detection with Vision Foundation Models”
- LinkedLinked via arxiv author · 85%Jiyao Liu →
“ViCo3D: Empowering LiDAR-based Collaborative 3D Object Detection with Vision Foundation Models”