Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction
Scaling 3D Gaussian Splatting (3DGS) to large outdoor scenes is costly in both data acquisition and computation. Adopting panoramic images with equirectangular projection (ERP) can reduce capture effort via their full $360^{\circ}$ field of view, yet the resulting omnipresent visibility invalidates existing partitioning strategies that rely on local camera frustums, causing block-wise optimization to degenerate into global training. Thus, we propose PanoLOG, a two-stage coarse-to-fine framework equipped with a Geometry and Gradient-based Partitioning Strategy tailored for large-scale panoramic
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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%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%Weijian Chen →
“Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction”
- LinkedLinked via arxiv author · 85%Weibo Yao →
“Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction”
- LinkedLinked via arxiv author · 85%Yuhang Zhang →
“Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction”
- LinkedLinked via arxiv author · 85%Xiaolin Tang →
“Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction”
- LinkedLinked via arxiv author · 85%Guo Wang →
“Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction”
- LinkedLinked via arxiv author · 85%Weijun Zhang →
“Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction”