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arxiv: 2605.26368 · v2 · pith:UYIA2VGHnew · submitted 2026-05-25 · 💻 cs.CV · cs.AI

Unified Panoramic Geometry Estimation via Multi-View Foundation Models

Vukasin Bozic , Isidora Slavkovic , Dominik Narnhofer , Nando Metzger , Denis Rozumny , Konrad Schindler , Nikolai Kalischek This is my paper

Pith reviewed 2026-06-29 22:06 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords panoramic geometrydepth estimationsurface normalsfoundation modelsomnidirectional imageszero-shot performance3D reconstruction
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The pith

PaGeR adapts pre-trained 3D foundation models to predict scale-invariant depth, metric depth, surface normals and sky masks from both perspective images and panoramas in one forward pass.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents PaGeR as a way to extend existing 3D reconstruction models from ordinary photos to full 360-degree panoramas without major redesign. It starts from a transformer already trained on perspective images, applies only small architectural tweaks, and trains on a mixture of both image types. The result is a single model that handles either input and outputs multiple geometry quantities at once while keeping the original 3D knowledge intact. This matters because it opens the door to recovering complete scene structure from a single panoramic shot, something that current perspective-only models cannot do directly.

Core claim

PaGeR lifts powerful 3D foundation models designed for perspective imagery to the panorama domain. The strategy keeps architectural changes to a minimum and mixes perspective and panoramic images during training so the model retains its rich 3D prior while also learning to estimate geometrically consistent 360-degree scenes from single panoramas. The unified model predicts scale-invariant depth, metric depth, surface normals, and sky masks from both perspective and omnidirectional images in a single forward pass.

What carries the argument

The minimally modified pre-trained transformer trained on mixed perspective and panoramic data that produces unified geometry outputs for both image domains.

If this is right

  • Achieves state-of-the-art performance on indoor and outdoor environments.
  • Delivers strong zero-shot generalization across a wide range of scenes.
  • Produces consistent full-scene geometry from a single panoramic input.
  • Supports both perspective and omnidirectional images without separate models or passes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same minimal-adaptation pattern could be tested on other geometry tasks such as optical flow or semantic segmentation.
  • If the mixed-training approach generalizes, it may reduce the need for fully separate panoramic training datasets in future work.
  • Applications that require quick 360 reconstruction, such as virtual walkthroughs, would benefit directly from a single-pass model.

Load-bearing premise

Mixing perspective and panoramic images during training together with only minimal architectural changes is enough to preserve the original 3D prior and avoid domain-specific inconsistencies in the 360-degree outputs.

What would settle it

A test showing that performance on perspective images drops or that panoramic outputs contain geometric inconsistencies after the mixed training would indicate the central claim does not hold.

Figures

Figures reproduced from arXiv: 2605.26368 by Denis Rozumny, Dominik Narnhofer, Isidora Slavkovic, Konrad Schindler, Nando Metzger, Nikolai Kalischek, Vukasin Bozic.

Figure 1
Figure 1. Figure 1: Different geometric modalities predicted by PaGeR. Given only a single monocular panoramic image, our framework simultaneously reconstructs highly detailed scale-invariant depth, absolute metric depth, surface normals, and sky segmentation masks across both indoor and outdoor environments. Spatial measurements are presented in meters. Abstract Geometry estimation from perspective images has greatly advance… view at source ↗
Figure 2
Figure 2. Figure 2: PaGeR Architecture. An input RGB panorama is processed by a shared geometry transformer backbone to predict a sky mask, scale-invariant (SI) depth, surface normals, and coarse metric depth. In the metric branch, the final absolute depth is obtained by aligning the SI depth with coarse metric predictions and masking the sky. In the normal branch, predicted orientations are masked by the sky segmentation to … view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative comparison of panoramic depth estimation. Visual results from PaGeR, DAP [27], and DA2 [23] (the strongest metric and scale-invariant baselines) alongside the RGB input and ground-truth depth on Matterport3D360, Stanford2D3DS, and ZüriPano. Our framework recovers sharper boundaries and more accurate global structures than competitors. Additional examples are in the appendix. Best viewed zoomed … view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison of panoramic surface normals estimation. Results from PaGeR and MTL (best available baseline method), shown alongside the RGB input and ground-truth depth on panoramas from the Structured3D dataset. (Best viewed zoomed in.) 8 [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Examples of RGB, depth, and surface normal panoramas from our PanoInfinigen dataset. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Samples of our ZüriPano dataset. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative comparison of panoramic depth estimation.. Visual results from PaGeR, compared to the subset of the best evaluated baselines, shown alongside the RGB input and ground￾truth depth on Matterport3D360, Stanford2D3DS, and ZüriPano panoramas. (Best viewed zoomed in.) Input DA3 Ours [PITH_FULL_IMAGE:figures/full_fig_p020_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Comparison to vanilla DA3. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative point-cloud comparison. Indoor scenes (top) and an outdoor scene (bottom) are rendered as point clouds alongside the corresponding panoramic input images for competitors and our method. For the indoor examples, we show our point cloud reconstruction with zoomed-in novel-view rendering comparison to the main competitors, highlighted by red boxes [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Examples of measured distances in our predicted point cloud. The measures are given in [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative comparison of panoramic surface normals estimation. Visual results from PaGeR and MTL (best available baseline method), shown alongside the RGB input and ground-truth depth on panoramas from the Structured3D dataset. (Best viewed zoomed in.) 22 [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
read the original 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. A natural extension is 3D reconstruction from panoramas, with the exciting prospect of recovering a full 360-degree scene from a single panoramic image. In this work, we introduce PaGeR (Panoramic Geometry Reconstruction), a framework to lift powerful 3D foundation models designed for perspective imagery to the panorama domain. Our strategy is to start from a pre-trained transformer for 3D reconstruction and turn it into a unified high-performance model that predicts scale-invariant depth, metric depth, surface normals, and sky masks from both perspective and omnidirectional images, in a single forward pass. By keeping architectural changes to a minimum and mixing perspective and panoramic images during training, PaGeR retains the rich 3D prior of the underlying foundation model while learning to also estimate geometrically consistent 360-degree scenes from single panoramas. We extensively test our method in both indoor and outdoor environments and find that it delivers state-of-the-art performance and excellent zero-shot performance across a wide range of scenes. Code, data and models are available $\href{https://github.com/prs-eth/PaGeR}{\text{here}}$.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces PaGeR, a framework that adapts pre-trained transformer-based 3D foundation models for perspective images to the panoramic domain. By making minimal architectural changes and training on a mix of perspective and panoramic images, it enables a single model to predict scale-invariant depth, metric depth, surface normals, and sky masks from both perspective and omnidirectional images in one forward pass. The authors claim state-of-the-art performance and strong zero-shot generalization across indoor and outdoor scenes.

Significance. If the experimental results hold, this work would be significant for providing a unified approach to 3D geometry estimation that bridges perspective and 360-degree imagery using foundation models. The release of code, data, and models enhances reproducibility and potential impact in computer vision applications involving panoramic scenes.

major comments (2)
  1. [Training procedure and ablations (likely §3-4)] The central claim that minimal architectural changes plus mixed training on perspective and panoramic data is sufficient to retain the base model's 3D prior (without domain interference from equirectangular distortions or wrap-around topology) is load-bearing but not yet substantiated by the provided abstract. Specific ablations comparing perspective-only performance before and after adding panoramic data are required to address the risk of compromised attention patterns or feature statistics.
  2. [Abstract and Experiments] The abstract asserts SOTA and excellent zero-shot results across scenes, but supplies no quantitative metrics, dataset details, or ablation evidence. Full experimental sections with tables reporting metrics on standard benchmarks (e.g., perspective depth/normal accuracy pre/post-mixing, panoramic consistency measures) would be required to assess whether the data support the claim.
minor comments (1)
  1. [Abstract] The abstract could benefit from including one or two key quantitative results (e.g., relative improvement on a panoramic benchmark) to ground the SOTA claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback. The comments highlight important aspects of substantiating our central claims regarding the mixed-training strategy and the need for clearer quantitative support in the abstract. We address each point below and indicate the revisions we will make.

read point-by-point responses

  1. Referee: [Training procedure and ablations (likely §3-4)] The central claim that minimal architectural changes plus mixed training on perspective and panoramic data is sufficient to retain the base model's 3D prior (without domain interference from equirectangular distortions or wrap-around topology) is load-bearing but not yet substantiated by the provided abstract. Specific ablations comparing perspective-only performance before and after adding panoramic data are required to address the risk of compromised attention patterns or feature statistics.

    Authors: We agree that explicit evidence of retained perspective performance after mixed training is essential to support the claim. While Section 4 already includes ablations on mixed vs. panoramic-only training and reports perspective-task metrics, it does not contain the exact before/after comparison on a held-out perspective benchmark. We will add this ablation (training the model on perspective data only, then continuing with mixed data, and evaluating both on standard perspective depth/normal benchmarks) to the revised manuscript. revision: yes

  2. Referee: [Abstract and Experiments] The abstract asserts SOTA and excellent zero-shot results across scenes, but supplies no quantitative metrics, dataset details, or ablation evidence. Full experimental sections with tables reporting metrics on standard benchmarks (e.g., perspective depth/normal accuracy pre/post-mixing, panoramic consistency measures) would be required to assess whether the data support the claim.

    Authors: The full manuscript already contains extensive experimental sections (Sections 4–5) with tables reporting quantitative metrics on standard perspective benchmarks (e.g., NYU, KITTI), panoramic datasets, zero-shot generalization, and consistency measures. However, the abstract is deliberately concise and omits specific numbers. We will revise the abstract to include a small number of key quantitative highlights (e.g., relative improvements on panoramic depth and zero-shot metrics) while keeping it within length limits, and ensure all requested table types are clearly referenced. revision: partial

Circularity Check

0 steps flagged

No circularity in derivation; empirical adaptation of external pre-trained model

full rationale

The paper presents an empirical adaptation strategy: starting from an external pre-trained transformer, applying minimal architectural changes, and training on mixed perspective/panoramic data. No equations, derivations, or self-defined quantities are shown that reduce to fitted inputs by construction. Claims rest on experimental validation against external benchmarks rather than self-citation chains or ansatzes imported from the authors' prior work. This matches the expected non-finding for papers whose central contribution is data mixing and fine-tuning without load-bearing self-referential math.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities beyond the high-level framework description; the pre-trained transformer is treated as an external input from prior literature.

pith-pipeline@v0.9.1-grok · 5791 in / 1202 out tokens · 35232 ms · 2026-06-29T22:06:01.644096+00:00 · methodology

discussion (0)

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