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Extending a Large View Synthesis Model for Multi-view Panoptic Segmentation

Research Multimodal & Generative

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Merged summary

TL;DR - A frozen large view-synthesis model can propagate panoptic labels across camera views, enabling 3D scene segmentation without explicit reconstruction or segmentation-specific model training. The approach matches reconstruction-based segmentation on ScanNet while improving novel-view synthesis and transferring to Replica without fine-tuning.

  • Encodes input-view panoptic labels as binary channels and renders target-view segmentations through learned cross-view attention.
  • Shows that spatial correspondences learned solely from RGB supervision generalize to view-independent per-pixel labels.
  • Matches Gaussian-based segmentation methods on ScanNet while exceeding their novel-view synthesis quality by more than 7 dB.
  • Outperforms those approaches on Replica without dataset-specific fine-tuning.

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Extending a Large View Synthesis Model for Multi-view Panoptic Segmentation

arXiv cs.CV Kwonyoung Ryu, In-Jae Lee, Jonghyun Jin, Hyunjee Lee, Jongmin Lee, Jaesik Park 2026-07-22 arXiv:2607.19765
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-20 14:34:43.208683 UTC

TL;DR - A frozen large view-synthesis model can propagate panoptic labels across camera views, enabling 3D scene segmentation without explicit reconstruction or segmentation-specific model training. The approach matches reconstruction-based segmentation on ScanNet while improving novel-view synthesis and transferring to Replica without fine-tuning.

  • Encodes input-view panoptic labels as binary channels and renders target-view segmentations through learned cross-view attention.
  • Shows that spatial correspondences learned solely from RGB supervision generalize to view-independent per-pixel labels.
  • Matches Gaussian-based segmentation methods on ScanNet while exceeding their novel-view synthesis quality by more than 7 dB.
  • Outperforms those approaches on Replica without dataset-specific fine-tuning.
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