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ARXIV:2603.26658 · COMPUTER VISION · SUBMITTED 30 MAR · 20:30 UTC · FRESHNESS STALE
ARXIV:2603.26658COMPUTER VISIONSUBMITTED 30 MAR · 20:30 UTCFRESHNESS STALEYiming Zuo · Hongyu Wen · Venkat Subramanian · Patrick Chen · Karhan Kayan · Mario Bijelic · +2 at arXiv
A novel Transformer-based architecture for zero-shot depth estimation from focus stacks, achieving significant performance improvements on a new real-world benchmark.
Opportunity summary
Pain A novel Transformer-based architecture for zero-shot depth estimation from focus stacks, achieving significant performance improvements on a new real-world benchmark.
Evidence 62 refs | 4 sources | 83% coverage
Blocker Evidence partial
A novel Transformer-based architecture for zero-shot depth estimation from focus stacks, achieving significant performance improvements on a new real-world benchmark. Unlike previous works overfitting to a certain dataset, this paper focuses on the challenging…
Depth from Defocus (DfD) is the task of estimating a dense metric depth map from a focus stack. Unlike previous works overfitting to a certain dataset, this paper focuses on the challenging and practical…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiment results on ZEDD and other benchmarks show a significant improvement over the baselines, reducing errors by up to 55.7%. A public repository is…
Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A novel Transformer-based architecture for zero-shot depth estimation from focus stacks, achieving significant performance improvements on a new real-world benchmark.
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10.48550/arXiv.2603.26658A novel Transformer-based architecture for zero-shot depth estimation from focus stacks, achieving significant performance improvements on a new real-world benchmark.
Abstract
Depth from Defocus (DfD) is the task of estimating a dense metric depth map from a focus stack. Unlike previous works overfitting to a certain dataset, this paper focuses on the challenging and practical setting of zero-shot generalization. We first propose a new real-world DfD benchmark ZEDD, which contains 8.3x more scenes and significantly higher quality images and ground-truth depth maps compared to previous benchmarks. We also design a novel network architecture named FOSSA. FOSSA is a Transformer-based architecture with novel designs tailored to the DfD task. The key contribution is a stack attention layer with a focus distance embedding, allowing efficient information exchange across the focus stack. Finally, we develop a new training data pipeline allowing us to utilize existing large-scale RGBD datasets to generate synthetic focus stacks. Experiment results on ZEDD and other benchmarks show a significant improvement over the baselines, reducing errors by up to 55.7%. The ZEDD benchmark is released at https://zedd.cs.princeton.edu. The code and checkpoints are released at https://github.com/princeton-vl/FOSSA.
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Proof status
partial62 refs; 4 sources; 83% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 7.0
PROBLEM
A novel Transformer-based architecture for zero-shot depth estimation from focus stacks, achieving significant performance improvements on a new real-world benchmark. Unlike previous works overfitting to a certain dataset, this paper focuses on the challenging and practical sett...
METHOD
Depth from Defocus (DfD) is the task of estimating a dense metric depth map from a focus stack. Unlike previous works overfitting to a certain dataset, this paper focuses on the challenging and practical setting of zero-shot generalization.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiment results on ZEDD and other benchmarks show a significant improvement over the baselines, reducing errors by up to 55.7%. A public repository is linked, so build verification can inspect implemen...
WHY NOW
Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
ZEDD has8.3×more scenes, higher quality ground-truth, higher resolution images, and realistic defocus effect under multiple f-numbers.
Directly stated in the abstract and supported by a comparison table.
partial
The key contribution is a stack attention layer with a focus distance embedding, allowing efficient information exchange across the focus stack.
The abstract and description of the network architecture clearly outline this novel component.
partial
Finally, we develop a new training data pipeline allowing us to utilize existing large-scale RGBD datasets to generate synthetic focus stacks.
Stated in the abstract as a key development for training.
partial
Experiment results on ZEDD and other benchmarks show a significant improvement over the baselines, reducing errors by up to 55.7%.
Directly stated in the abstract with a specific quantitative result.
partial
It reduces AbsRel by 55.7% compared to the second-best method DepthPro [3] on ZEDD, and MSE by 40.4% on DDFF compared to previous state-of-the-art DualFocus [49].
Specific quantitative result comparing FOSSA to a baseline on a key metric and dataset.
partial
We also demonstrate that FOSSA is robust to various factors, including the aperture size and the focus stack size.
Stated as a demonstrated capability of the FOSSA method.
partial
Ours (ViT-B) 0.505 0.824 0.918 0.089 0.420 0.820 0.936 0.091
Directly reported in Table 2 with specific model variant and metric.
partial
ZEDD has8.3×more scenes, higher quality ground-truth, higher resolution images, and realistic defocus effect under multiple f-numbers.
This claim is directly stated in the abstract and supported by the comparison in Table 1.
partial
The key contribution is a stack attention layer with a focus distance embedding, allowing efficient information exchange across the focus stack.
This claim is explicitly described in the abstract and further detailed in the description of the network architecture.
partial
Experiment results on ZEDD and other benchmarks show a significant improvement over the baselines, reducing errors by up to 55.7%.
This claim is directly stated in the abstract and supported by the experimental results presented in the text and Table 2.
partial
It reduces AbsRel by 55.7% compared to the second-best method DepthPro [3] on ZEDD, and MSE by 40.4% on DDFF compared to previous state-of-the-art DualFocus [49].
This is a specific quantitative result mentioned in the abstract and elaborated in the text.
partial
Our method FOSSA outperforms all monocular d
This claim is directly supported by the comparison in Table 2, which shows FOSSA achieving better performance metrics than other listed methods.
partial
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Concepts
Methods
Materials
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A novel Transformer-based architecture for zero-shot depth estimation from focus stacks, achieving significant performance improvements on a new real-world benchmark.
Segment
Computer Vision
Adoption evidence
Public code linked for build inspection
Commercial read
7.0/10 public viability
Direct
Adjacent
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CITED BY
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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
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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.
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Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
62 refs / 4 sources / 83% 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
62 references, 4 sources, 83% 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
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Map target operator, economic buyer, and procurement trigger.
Defensibility
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Defensibility signals are missing.
Evidence
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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
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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
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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People
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Regulatory need unclassified.
No clinical or regulatory source attached.
People
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
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