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PaGeR framework adapts 3D models for 360-degree panoramic scene reconstruction

By PulseAugur Editorial · [2 sources] ·

Researchers have developed PaGeR, a framework that adapts existing 3D foundation models, originally designed for perspective images, to reconstruct full 360-degree scenes from single panoramic images. This approach allows for a unified, single-pass estimation of scale-invariant depth, metric depth, surface normals, and sky masks. By minimizing architectural changes and training with a mix of perspective and panoramic data, PaGeR retains the underlying model's 3D prior while enabling consistent 360-degree scene estimation, achieving state-of-the-art performance. AI

IMPACT Enables reconstruction of full 360-degree scenes from single images, potentially advancing applications in robotics, VR, and autonomous systems.

RANK_REASON The cluster describes a new research paper detailing a novel framework for 3D geometry estimation.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

PaGeR framework adapts 3D models for 360-degree panoramic scene reconstruction

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The cluster describes a new research paper detailing a novel framework for 3D geometry estimation.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Vukasin Bozic, Isidora Slavkovic, Dominik Narnhofer, Nando Metzger, Denis Rozumny, Konrad Schindler, Nikolai Kalischek ·

    Unified Panoramic Geometry Estimation via Multi-View Foundation Models

    arXiv:2605.26368v1 Announce Type: cross Abstract: Geometry estimation from perspective images has greatly advanced, maturing to the point where off-the-shelf foundation models are able to reconstruct 3D scene structure not only from multi-view imagery, but even from a single view…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Unified Panoramic Geometry Estimation via Multi-View Foundation Models

    PaGeR is a framework that adapts 3D foundation models for perspective imagery to reconstruct 360-degree scenes from panoramic images, enabling simultaneous prediction of depth, normals, and sky masks with high performance.

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