ARXIV:2603.08131 · 3D VISUAL GROUNDING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing

arXiv

UniGround enables zero-shot 3D visual grounding by using training-free scene parsing, offering a robust solution for localizing objects in complex 3D environments.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain UniGround enables zero-shot 3D visual grounding by using training-free scene parsing, offering a robust solution for localizing objects in complex 3D environments.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

UniGround enables zero-shot 3D visual grounding by using training-free scene parsing, offering a robust solution for localizing objects in complex 3D environments. Large-scale pre-trained foundation models have driven significant progress on this front, enabling…

METHOD

Full abstract

Understanding and localizing objects in complex 3D environments from natural language descriptions, known as 3D Visual Grounding (3DVG), is a foundational challenge in embodied AI, with broad implications for robotics, augmented reality, and human-machine interaction. Large-scale pre-trained foundation models have driven significant progress on this front, enabling open-vocabulary 3DVG that allows systems to locate arbitrary objects in a given scene. However, their reliance on pre-trained models constrains 3D perception and reasoning within the inherited knowledge boundaries, resulting in limited generalization to unseen spatial relationships and poor robustness to out-of-distribution scenes. In this paper, we replace this constrained perception with training-free visual and geometric reasoning, thereby unlocking open-world 3DVG that enables the localization of any object in any scene beyond the training data. Specifically, the proposed UniGround operates in two stages: a Global Candidate Filtering stage that constructs scene candidates through training-free 3D topology and multi-view semantic encoding, and a Local Precision Grounding stage that leverages multi-scale visual prompting and structured reasoning to precisely identify the target object. Experiments on ScanRefer and EmbodiedScan show that UniGround achieves 46.1\%/34.1\% Acc@0.25/0.5 on ScanRefer and 28.7\% Acc@0.25 on EmbodiedScan, establishing a new state-of-the-art among zero-shot methods on EmbodiedScan without any 3D supervision. We further evaluate UniGround in real-world environments under uncontrolled reconstruction conditions and substantial domain shift, showing training-free reasoning generalizes robustly beyond curated benchmarks.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this paper, we replace this constrained perception with training-free visual and geometric reasoning, thereby unlocking open-world 3DVG that enables the localization of any…

WHY NOW

3D Visual Grounding moved forward this cycle; last verified April 2026. Public score 7.0/10.

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

Opportunity summary

Score7.0

PainUniGround enables zero-shot 3D visual grounding by using training-free scene parsing, offering a robust solution for localizing objects in complex 3D environments.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

UniGround enables zero-shot 3D visual grounding by using training-free scene parsing, offering a robust solution for localizing objects in complex 3D environments.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.08131 · 3D VISUAL GROUNDING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing

arXiv

UniGround enables zero-shot 3D visual grounding by using training-free scene parsing, offering a robust solution for localizing objects in complex 3D environments.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain UniGround enables zero-shot 3D visual grounding by using training-free scene parsing, offering a robust solution for localizing objects in complex 3D environments.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

3D Visual Grounding moved forward this cycle; last verified April 2026. Public score 7.0/10.

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

Opportunity summary

Score7.0

PainUniGround enables zero-shot 3D visual grounding by using training-free scene parsing, offering a robust solution for localizing objects in complex 3D environments.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

UniGround enables zero-shot 3D visual grounding by using training-free scene parsing, offering a robust solution for localizing objects in complex 3D environments.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.08131

UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing

UniGround enables zero-shot 3D visual grounding by using training-free scene parsing, offering a robust solution for localizing objects in complex 3D environments.

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 fallback46 indexednot indexed

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

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

3D Visual Grounding moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
UniGround enables zero-shot 3D visual grounding by using training-free scene parsing, offering a robust solution for localizing objects in complex 3D environments. Large-scale pre-trained foundation models have driven significant progress on this front, enabling open-vocabulary 3DVG that allows systems to locate arbitrary objects in a given scene.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Understanding and localizing objects in complex 3D environments from natural language descriptions, known as 3D Visual Grounding (3DVG), is a foundational challenge in embodied AI, with broad implications for robotics, augmented reality, and human-machine interaction. Large-scale pre-trained foundation models have driven significant progress on this front, enabling open-vocabulary 3DVG that allows systems to locate arbitrary objects in a given scene.
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. In this paper, we replace this constrained perception with training-free visual and geometric reasoning, thereby unlocking open-world 3DVG that enables the localization of any object in any scene beyond the training data.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
3D Visual Grounding moved forward this cycle; last verified April 2026. Public score 7.0/10.
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

3D Visual Grounding

Competitors

not indexed

Competitive landscape

UniGround enables zero-shot 3D visual grounding by using training-free scene parsing, offering a robust solution for localizing objects in complex 3D environments.

Segment

3D Visual Grounding

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 2603.08131 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

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

References(46)

Reference metadata pending (4a012100c41cf05a9637ac422e52717fca79242a)

Reference metadata pending (06f6110a37866dafb662a4ff3dedfc4bae55f302)

Reference metadata pending (0cc970e041ad0360b906f92f4031e7a32d958c1c)

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Reference metadata pending (db8a233e1ea5ff87c361ff352543853f67e7250d)

Reference metadata pending (b8710b0c484fc703ab8cbe8d1733a5bd7500f927)

Reference metadata pending (7a01deadb36aa5386949f71feb1599d951c5b409)

Reference metadata pending (250deac0cf7153fea2cbbdab18818d0e262e256c)

Reference metadata pending (81537839e923b859490042e861217764524baacc)

Reference metadata pending (9ea340b113ff0681eb9bbaa2c002f86c54c7b85e)

Reference metadata pending (a0711a4f555693623fc001de38b3c8f1223c0888)

Reference metadata pending (28afc4d43909e6071fedbf4f29e02ddc81d29fe7)

Reference metadata pending (839f73c8db2a35e10c5e7f8e20dfbaa2d5fce6ba)

Reference metadata pending (2d0c42dd3592879ac7608310a81bc4275712140f)

Reference metadata pending (f1e19216db7b90b793ce33d8d8dfe225bef18d89)

Reference metadata pending (214ff1d7afcd31477997805f0e6cfa517bc8f00c)

Reference metadata pending (2f91ae9e841953814c188a213f362de3d33d3a5e)

Reference metadata pending (12693672913f00a32b31fe68a3d0d3d4c40cc352)

Reference metadata pending (55d184e7bcf467f2824cd9603194e523b206f8e7)

Showing 20 of 46 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 WorkUnifying UAV Cross-View Geo-Localization via 3D Geometric Perception

7.0

Prior WorkGeoGuide: Hierarchical Geometric Guidance for Open-Vocabulary 3D Semantic Segmentation

7.0

Prior WorkScene Grounding In the Wild

7.0

Prior WorkProVG: Progressive Visual Grounding via Language Decoupling for Remote Sensing Imagery

7.0

Prior WorkPart-Aware Open-Vocabulary 3D Affordance Grounding via Prototypical Semantic and Geometric Alignment

7.0

Prior WorkUrbanGraphEmbeddings: Learning and Evaluating Spatially Grounded Multimodal Embeddings for Urban Science

7.0

Extension

Builds On ThisUniSem: Generalizable Semantic 3D Reconstruction from Sparse Unposed Images

2.0

Commercially relevant

Higher ViabilityThink, Act, Build: An Agentic Framework with Vision Language Models for Zero-Shot 3D Visual Grounding

8.0

Higher ViabilityGeM-VG: Towards Generalized Multi-image Visual Grounding with Multimodal Large Language Models

8.0

Higher ViabilityTrianguLang: Geometry-Aware Semantic Consensus for Pose-Free 3D Localization

8.0

Conflicting

none indexed

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

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: 2603.08131
Route: /paper/uniground-universal-3d-visual-grounding-via-training-free-scene-parsing
Active tab: read
Artifact: uniground-universal-3d-visual-grounding-via-training-free-scene-parsing

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/uniground-universal-3d-visual-grounding-via-training-free-scene-parsing/paper-pack
REST build passport API/api/v1/paper/uniground-universal-3d-visual-grounding-via-training-free-scene-parsing/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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  "arxiv_id": "2603.08131",
  "canonical_route": "/paper/uniground-universal-3d-visual-grounding-via-training-free-scene-parsing",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "uniground-universal-3d-visual-grounding-via-training-free-scene-parsing",
  "endpoints": {
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    "build_passport": "/api/v1/paper/uniground-universal-3d-visual-grounding-via-training-free-scene-parsing/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

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/uniground-universal-3d-visual-grounding-via-training-free-scene-parsing

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

UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing

Canonical ID uniground-universal-3d-visual-grounding-via-training-free-scene-parsing | Route /paper/uniground-universal-3d-visual-grounding-via-training-free-scene-parsing

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/uniground-universal-3d-visual-grounding-via-training-free-scene-parsing

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.08131"
  }
}

source_context

{
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  "mode": "paper",
  "query": "UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing",
  "normalized_query": "2603.08131",
  "route": "/paper/uniground-universal-3d-visual-grounding-via-training-free-scene-parsing",
  "paper_ref": "uniground-universal-3d-visual-grounding-via-training-free-scene-parsing",
  "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: UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing

/buildability/uniground-universal-3d-visual-grounding-via-training-free-scene-parsing

Watchwatch

Subject: UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing

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/uniground-universal-3d-visual-grounding-via-training-free-scene-parsing

Paper ref

uniground-universal-3d-visual-grounding-via-training-free-scene-parsing

arXiv id

2603.08131

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

e3a3cebcfffd9a81a24277c31cf8fbbfc6872ea840d6e58840fc7f4c0ed48f57

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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UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing

UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing

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