Seen2Scene: Completing Realistic 3D Scenes with Visibility-Guided Flow

Seen2Scene: Completing Realistic 3D Scenes with Visibility-Guided Flow | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/seen2scene-completing-realistic-3d-scenes-with-visibility-guided-flow

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 84
Source count: 3
Coverage: 50%

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

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Canonical ID seen2scene-completing-realistic-3d-scenes-with-visibility-guided-flow | Route /signal-canvas/seen2scene-completing-realistic-3d-scenes-with-visibility-guided-flow

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/seen2scene-completing-realistic-3d-scenes-with-visibility-guided-flow

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: 84

Proof: Verification pending

Freshness state: computing

Source paper: Seen2Scene: Completing Realistic 3D Scenes with Visibility-Guided Flow

PDF: https://arxiv.org/pdf/2603.28548v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-31T20:17:51.762Z

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Watch and verify: Seen2Scene: Completing Realistic 3D Scenes with Visibility-Guided Flow

/buildability/seen2scene-completing-realistic-3d-scenes-with-visibility-guided-flow

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Subject: Seen2Scene: Completing Realistic 3D Scenes with Visibility-Guided Flow

Verdict

Watch

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

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
We present Seen2Scene, the first flow matching-based approach that trains directly on incomplete, real-world 3D scans for scene completion and generation.
Implicationpartial
Explicitly stated in the abstract as a primary contribution of the paper.
Verificationpartial
partial
Evidencepartial
our approach introduces visibility-guided flow matching, which explicitly masks out unknown regions in real scans, enabling effective learning from real-world, partial observations.
Implicationpartial
Directly stated in the abstract as the core methodological innovation, with ablation study (Fig. 7) showing its necessity.
Verificationpartial
partial
Evidencepartial
Our model without bounding boxes achieves comparable performance to its bounding box-conditioned counterpart, and even yields better overall structural correctness (CD).
Implicationpartial
Supported by quantitative results in Tab. 1 (referenced in text) showing better metrics for Seen2Scene.
Verificationpartial
partial
Evidencepartial
Our method produces more geometrically detailed and semantically coherent scenes compared to BlockFusion [61], LT3SD [42], and WorldGrow [34].
Implicationpartial
Qualitative claim made in caption of Fig. 5 and supported by quantitative metrics in a table (DINOv2-FID, U3D-FPD, VLM Score).
Verificationpartial
partial
Evidencepartial
Without two stages of masked training, the model tends to generate holes (under the table) following the training incomplete scans.
Implicationpartial
Directly stated in the analysis of the ablation study (Fig. 7) with a specific example.
Verificationpartial
partial
Evidencepartial
Open Vocabulary Semantic Encodings.We ablate the effect of using CLIP embedding [46] instead of one-hot semantic labels which has a fixed number of categories
Implicationpartial
Implied by the ablation study in Tab. 4 and Fig. 8, which discusses the challenge of category mapping with fixed labels.
Verificationpartial
partial
Evidencepartial
Text to scene.Given a text description, Seen2Scene generates a 3D object layout first via an
Implicationpartial
Explicitly stated and demonstrated with qualitative examples in Fig. 6.
Verificationpartial
partial
Evidencepartial
training only on 3D-FRONT [20], or with limited categories, or without label synonym augmentation, reduces the model’s generalization ability to diverse object layouts, like 'clothes on an office chair.'
Implicationpartial
Stated as a finding from an ablation study, though the specific performance degradation is not quantified.
Verificationpartial
partial

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Seen2Scene: Completing Realistic 3D Scenes with Visibility-Guided Flow

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