JADE-GS: Joint Alternating Deblurring Guided by Events in 3D Gaussian Splatting
When a camera moves fast during exposure, blur destroys the intra-exposure motion a 3D model needs to recover the sharp scene, while event cameras capture exactly this signal at microsecond resolution. Turning them into reliable 3D supervision faces two obstacles. First, the two restoration priors fail in opposite ways: physics-based event-integration priors preserve edges but accumulate drift; learned networks recover texture but distort boundaries. Second, existing pipelines run in one direction only, so raw event noise or the biases of fixed 2D pseudo-labels pass uncorrected into the geomet
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Haoyu Fu →
“JADE-GS: Joint Alternating Deblurring Guided by Events in 3D Gaussian Splatting”
- LinkedLinked via arxiv author · 85%Jiafeng Huang →
“JADE-GS: Joint Alternating Deblurring Guided by Events in 3D Gaussian Splatting”
- LinkedLinked via arxiv author · 85%Yuchen Wang →
“JADE-GS: Joint Alternating Deblurring Guided by Events in 3D Gaussian Splatting”
- LinkedLinked via arxiv author · 85%Shengjie Zhao →
“JADE-GS: Joint Alternating Deblurring Guided by Events in 3D Gaussian Splatting”