USR-Drive: Unified Driving Scene Representation via Joint Denoising of 3D Gaussians and Boxes
Spatial representation learning for autonomous driving aims to map raw visual signals into structured 3D scene representations, where object-centric bounding boxes and rendering-oriented 3D primitives (\eg, 3D Gaussians) serve as two distinct yet highly complementary levels for scene understanding. Existing methods typically treat dynamic reconstruction and instance-level perception as separate tasks, despite their shared goal of estimating the underlying 3D world state. As a result, dynamic reconstruction is under-constrained while 3D detection lacks geometric grounding. To address this gap,
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- LinkedLinked via arxiv author · 85%Li-Heng Chen →
“USR-Drive: Unified Driving Scene Representation via Joint Denoising of 3D Gaussians and Boxes”
- LinkedLinked via arxiv author · 85%Haokai Pang →
“USR-Drive: Unified Driving Scene Representation via Joint Denoising of 3D Gaussians and Boxes”
- LinkedLinked via arxiv author · 85%Chengye Su →
“USR-Drive: Unified Driving Scene Representation via Joint Denoising of 3D Gaussians and Boxes”
- LinkedLinked via arxiv author · 85%Jiarun Liu →
“USR-Drive: Unified Driving Scene Representation via Joint Denoising of 3D Gaussians and Boxes”
- LinkedLinked via arxiv author · 85%Qifeng Chen →
“USR-Drive: Unified Driving Scene Representation via Joint Denoising of 3D Gaussians and Boxes”
- LinkedLinked via arxiv author · 85%Ziqian Ni →
“USR-Drive: Unified Driving Scene Representation via Joint Denoising of 3D Gaussians and Boxes”
- LinkedLinked via arxiv author · 85%Jianxin Huang →
“USR-Drive: Unified Driving Scene Representation via Joint Denoising of 3D Gaussians and Boxes”