ARXIV:2603.18943 · COMPUTER VISION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation

Jiayi Yuan · Haobo Jiang · De Wen Soh · Na Zhao · arXiv

A zero-shot panoramic depth estimation framework that leverages 3D consistency from foundation models to achieve state-of-the-art accuracy without training.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain A zero-shot panoramic depth estimation framework that leverages 3D consistency from foundation models to achieve state-of-the-art accuracy without training.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A zero-shot panoramic depth estimation framework that leverages 3D consistency from foundation models to achieve state-of-the-art accuracy without training. Unlike prior view-independent training-free approaches, VGGT-360 reformulates the task as panoramic reprojection over multi-view reconstructed…

METHOD

Full abstract

This paper presents VGGT-360, a novel training-free framework for zero-shot, geometry-consistent panoramic depth estimation. Unlike prior view-independent training-free approaches, VGGT-360 reformulates the task as panoramic reprojection over multi-view reconstructed 3D models by leveraging the intrinsic 3D consistency of VGGT-like foundation models, thereby unifying fragmented per-view reasoning into a coherent panoramic understanding. To achieve robust and accurate estimation, VGGT-360 integrates three plug-and-play modules that form a unified panorama-to-3D-to-depth framework: (i) Uncertainty-guided adaptive projection slices panoramas into perspective views to bridge the domain gap between panoramic inputs and VGGT's perspective prior. It estimates gradient-based uncertainty to allocate denser views to geometry-poor regions, yielding geometry-informative inputs for VGGT. (ii) Structure-saliency enhanced attention strengthens VGGT's robustness during 3D reconstruction by injecting structure-aware confidence into its attention layers, guiding focus toward geometrically reliable regions and enhancing cross-view coherence. (iii) Correlation-weighted 3D model correction refines the reconstructed 3D model by reweighting overlapping points using attention-inferred correlation scores, providing a consistent geometric basis for accurate panoramic reprojection. Extensive experiments show that VGGT-360 outperforms both trained and training-free state-of-the-art methods across multiple resolutions and diverse indoor and outdoor datasets.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. To achieve robust and accurate estimation, VGGT-360 integrates three plug-and-play modules that form a unified panorama-to-3D-to-depth framework: (i) Uncertainty-guided adaptive projection slices panoramas into…

WHY NOW

Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA zero-shot panoramic depth estimation framework that leverages 3D consistency from foundation models to achieve state-of-the-art accuracy without training.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

A zero-shot panoramic depth estimation framework that leverages 3D consistency from foundation models to achieve state-of-the-art accuracy without training.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

ARXIV:2603.18943 · COMPUTER VISION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation

Jiayi Yuan · Haobo Jiang · De Wen Soh · Na Zhao · arXiv

A zero-shot panoramic depth estimation framework that leverages 3D consistency from foundation models to achieve state-of-the-art accuracy without training.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain A zero-shot panoramic depth estimation framework that leverages 3D consistency from foundation models to achieve state-of-the-art accuracy without training.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA zero-shot panoramic depth estimation framework that leverages 3D consistency from foundation models to achieve state-of-the-art accuracy without training.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

A zero-shot panoramic depth estimation framework that leverages 3D consistency from foundation models to achieve state-of-the-art accuracy without training.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.18943

VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation

A zero-shot panoramic depth estimation framework that leverages 3D consistency from foundation models to achieve state-of-the-art accuracy without training.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback50 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

code

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A zero-shot panoramic depth estimation framework that leverages 3D consistency from foundation models to achieve state-of-the-art accuracy without training. Unlike prior view-independent training-free approaches, VGGT-360 reformulates the task as panoramic reprojection over multi-view reconstructed 3D models by leveraging the intrinsic 3D consistency of VGGT-like foundation models, thereby unifying fragmented per-view reasoning into a coherent panoramic understanding.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
This paper presents VGGT-360, a novel training-free framework for zero-shot, geometry-consistent panoramic depth estimation. Unlike prior view-independent training-free approaches, VGGT-360 reformulates the task as panoramic reprojection over multi-view reconstructed 3D models by leveraging the intrinsic 3D consistency of VGGT-like foundation models, thereby unifying fragmented per-view reasoning into a coherent panoramic understanding.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To achieve robust and accurate estimation, VGGT-360 integrates three plug-and-play modules that form a unified panorama-to-3D-to-depth framework: (i) Uncertainty-guided adaptive projection slices panoramas into perspective views to bridge the domain gap between panoramic inputs and VGGT's perspective prior. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Computer Vision

Competitors

not indexed

Competitive landscape

A zero-shot panoramic depth estimation framework that leverages 3D consistency from foundation models to achieve state-of-the-art accuracy without training.

Segment

Computer Vision

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.18943 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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Bluesky

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(50)

VGGT-X: When VGGT Meets Dense Novel View Synthesis

2025Yang Liu, Chuanchen Luo et al.

RPG360: Robust 360 Depth Estimation with Perspective Foundation Models and Graph Optimization

2025Dongki Jung, Jaehoon Choi et al.

VGGT-DP: Generalizable Robot Control via Vision Foundation Models

2025Shijia Ge, Yinxin Zhang et al.

Improving Robotic Manipulation with Efficient Geometry-Aware Vision Encoder

2025Vuong Dinh An, Minh Nhat Vu et al.

FastVGGT: Training-Free Acceleration of Visual Geometry Transformer

2025You Shen, Zhipeng Zhang et al.

OmniStereo: Real-Time Omnidireactional Depth Estimation with Multiview Fisheye Cameras

2025Jiaxi Deng, Yushen Wang et al.

VGGT: Visual Geometry Grounded Transformer

2025Jianyuan Wang, Minghao Chen et al.

Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera

2025Yuliang Guo, Sparsh Garg et al.

π3: Scalable Permutation-Equivariant Visual Geometry Learning

2025Yifan Wang, Jianjun Zhou et al.

PanDA: Towards Panoramic Depth Anything with Unlabeled Panoramas and Möbius Spatial Augmentation

2024Zidong Cao, Jinjin Zhu et al.

Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation

2024Ning-Hsu Wang, Yu-Lun Liu

MS360: A Multi-Scale Feature Fusion Framework for 360 Monocular Depth Estimation

2024Payal Mohadikar, Chuanmao Fan et al.

CRF360D: Monocular 360 Depth Estimation via Spherical Fully-Connected CRFs

2024Zidong Cao, Lin Wang

SGFormer: Spherical Geometry Transformer for 360 Depth Estimation

2024Junsong Zhang, Zisong Chen et al.

Elite360D: Towards Efficient 360 Depth Estimation via Semantic- and Distance-Aware Bi-Projection Fusion

2024Hao Ai, Lin Wang

Metric3D v2: A Versatile Monocular Geometric Foundation Model for Zero-Shot Metric Depth and Surface Normal Estimation

2024Mu Hu, Wei Yin et al.

Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

2024Lihe Yang, Bingyi Kang et al.

SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation

2023Haobo Jiang, Mathieu Salzmann et al.

Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image

2023Wei Yin, Chi Zhang et al.

EGformer: Equirectangular Geometry-biased Transformer for 360 Depth Estimation

2023Ilwi Yun, Chanyong Shin et al.

Showing 20 of 50 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkUnified Panoramic Geometry Estimation via Multi-View Foundation Models

7.0

Prior WorkIVGT: Implicit Visual Geometry Transformer for Neural Scene Representation

7.0

Prior WorkPanoWorld: Geometry-Consistent Panoramic Video World Modeling

7.0

Prior WorkGGPT: Geometry Grounded Point Transformer

7.0

Prior WorkEndoVGGT: GNN-Enhanced Depth Estimation for Surgical 3D Reconstruction

7.0

Prior WorkEventVGGT: Exploring Cross-Modal Distillation for Consistent Event-based Depth Estimation

7.0

Prior WorkVGGT-World: Transforming VGGT into an Autoregressive Geometry World Model

7.0

Extension

Builds On ThisVGGT-Edit: Feed-forward Native 3D Scene Editing with Residual Field Prediction

3.0

Commercially relevant

Higher ViabilityPanoVGGT: Feed-Forward 3D Reconstruction from Panoramic Imagery

8.0

Conflicting

Competing ApproachPano360: Perspective to Panoramic Vision with Geometric Consistency

7.0

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.18943
Route: /paper/vggt-360-geometry-consistent-zero-shot-panoramic-depth-estimation
Active tab: read
Artifact: vggt-360-geometry-consistent-zero-shot-panoramic-depth-estimation

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/vggt-360-geometry-consistent-zero-shot-panoramic-depth-estimation/paper-pack
REST build passport API/api/v1/paper/vggt-360-geometry-consistent-zero-shot-panoramic-depth-estimation/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "vggt-360-geometry-consistent-zero-shot-panoramic-depth-estimation",
  "endpoints": {
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}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/vggt-360-geometry-consistent-zero-shot-panoramic-depth-estimation

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation

Canonical ID vggt-360-geometry-consistent-zero-shot-panoramic-depth-estimation | Route /paper/vggt-360-geometry-consistent-zero-shot-panoramic-depth-estimation

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/vggt-360-geometry-consistent-zero-shot-panoramic-depth-estimation

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.18943"
  }
}

source_context

{
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  "mode": "paper",
  "query": "VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation",
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  "paper_ref": "vggt-360-geometry-consistent-zero-shot-panoramic-depth-estimation",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation

/buildability/vggt-360-geometry-consistent-zero-shot-panoramic-depth-estimation

Watchwatch

Subject: VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/vggt-360-geometry-consistent-zero-shot-panoramic-depth-estimation

Paper ref

vggt-360-geometry-consistent-zero-shot-panoramic-depth-estimation

arXiv id

2603.18943

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

194a67e390d8530f04557ce561c2b514d5785752e39e984bd1e0bad06bd5e394

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation

VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation

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Brief

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Regulatory/clinical advisor

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