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ARXIV:2604.00548 · 3D RECONSTRUCTION · SUBMITTED 02 APR · 21:00 UTC · FRESHNESS STALE
ARXIV:2604.005483D RECONSTRUCTIONSUBMITTED 02 APR · 21:00 UTCFRESHNESS STALEYouyu Chen · Junjun Jiang · Yueru Luo · Kui Jiang · Xianming Liu · Xu Yan · +1 at arXiv
A weakly-supervised paradigm for training feed-forward 3D reconstruction models using only relative depths and sparse correspondences.
Opportunity summary
Pain A weakly-supervised paradigm for training feed-forward 3D reconstruction models using only relative depths and sparse correspondences.
Evidence 48 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A weakly-supervised paradigm for training feed-forward 3D reconstruction models using only relative depths and sparse correspondences. However, the excessive reliance on multi-view geometric annotations, e.g.
With recent advances, Feed-forward Reconstruction Models (FFRMs) have demonstrated great potential in reconstruction quality and adaptiveness to multiple downstream tasks. However, the excessive reliance on multi-view geometric annotations, e.g.
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Training from scratch with the less data, Reliev3R catches up with its fully-supervised sibling models, taking a step towards low-cost 3D reconstruction supervisions and…
3D Reconstruction moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score6.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A weakly-supervised paradigm for training feed-forward 3D reconstruction models using only relative depths and sparse correspondences.
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Paper Pack
10.48550/arXiv.2604.00548A weakly-supervised paradigm for training feed-forward 3D reconstruction models using only relative depths and sparse correspondences.
Abstract
With recent advances, Feed-forward Reconstruction Models (FFRMs) have demonstrated great potential in reconstruction quality and adaptiveness to multiple downstream tasks. However, the excessive reliance on multi-view geometric annotations, e.g. 3D point maps and camera poses, makes the fully-supervised training scheme of FFRMs difficult to scale up. In this paper, we propose Reliev3R, a weakly-supervised paradigm for training FFRMs from scratch without cost-prohibitive multi-view geometric annotations. Relieving the reliance on geometric sensory data and compute-exhaustive structure-from-motion preprocessing, our method draws 3D knowledge directly from monocular relative depths and image sparse correspondences given by zero-shot predictions of pretrained models. At the core of Reliev3R, we design an ambiguity-aware relative depth loss and a trigonometry-based reprojection loss to facilitate supervision for multi-view geometric consistency. Training from scratch with the less data, Reliev3R catches up with its fully-supervised sibling models, taking a step towards low-cost 3D reconstruction supervisions and scalable FFRMs.
Source availability
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Extraction status
Parse run linkedA document parse run is attached to this paper.
Proof status
unverified48 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 6.0
PROBLEM
A weakly-supervised paradigm for training feed-forward 3D reconstruction models using only relative depths and sparse correspondences. However, the excessive reliance on multi-view geometric annotations, e.g.
METHOD
With recent advances, Feed-forward Reconstruction Models (FFRMs) have demonstrated great potential in reconstruction quality and adaptiveness to multiple downstream tasks. However, the excessive reliance on multi-view geometric annotations, e.g.
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Training from scratch with the less data, Reliev3R catches up with its fully-supervised sibling models, taking a step towards low-cost 3D reconstruction supervisions and scalable FFRMs. Code availability...
WHY NOW
3D Reconstruction moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A weakly-supervised paradigm for training feed-forward 3D reconstruction models using only relative depths and sparse correspondences. However, the excessive reliance on multi-view geometric annotations, e.g.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
With recent advances, Feed-forward Reconstruction Models (FFRMs) have demonstrated great potential in reconstruction quality and adaptiveness to multiple downstream tasks. However, the excessive reliance on multi-view geometric annotations, e.g.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Training from scratch with the less data, Reliev3R catches up with its fully-supervised sibling models, taking a step towards low-cost 3D reconstruction supervisions and scalable FFRMs. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
3D Reconstruction moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
Markets
Competitors
A weakly-supervised paradigm for training feed-forward 3D reconstruction models using only relative depths and sparse correspondences.
Segment
3D Reconstruction
Adoption evidence
No public code link in the paper record yet
Commercial read
6.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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Hacker News
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Bluesky
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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
Commercially relevant
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3/3 checks · 100%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
48 refs / 3 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
partial
Current read
Research evidence exists; buyer urgency still needs source proof.
Evidence
48 references, 3 sources, 50% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
Next test
Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
No verified watchtower monitor rows yet.
FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
No verified OpportunityKernel changes since the last view.
COMPETITIVE LANDSCAPE UPDATES
No verified competitive landscape changes yet.
RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
Save this paper to start tracking momentum - commits, demos, and score changes appear here.
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Score trend will appear after multiple data points.
BUZZ
Buzz trend pending.