How reproducible is "Leveraging Blur Information for Plenoptic Camera Calibration"?

Estimated time to first reproduction: a few days. Risk flags: No repository-level reproducibility signals are currently available, Estimate is based on paper-only reproduction flow. No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.

Leveraging Blur Information for Plenoptic Camera Calibration

Mathieu Labussière, Céline Teulière, Frédéric Bernardin, Omar Ait-Aider

Published: May 4, 2022

No direct implementation yet

Evidence: Inferred

Domain fit: AI-adjacent

Verified repos: 0

Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.

Time to first repro: a few days

2 risk flags

DOI Publisher

Technical details

Canonical key: doi-10.1007_s11263-022-01582-z

Cache status: Stale (SWR served)

Generated at: Jun 18, 2026, 9:35 PM

Artifact coverage: sparse

HF provider: ok (mixed)

PWC source used: No

LLM status: not_generated

LLM model: n/a

LLM generated: Unknown

LLM content type: n/a

HF policy: hf-relevance-v27

context only

Benchmarks: missing

Time to repro: a few days

2 risk flags

Results & Benchmarks

Freshness tier: cold

Direct + Inferred Evidence

No concrete benchmark grounding is available yet. Treat the page as context or an implementation starting point only.

Leveraging Blur Information for Plenoptic Camera Calibration focuses on computer science.

Implementation Evidence Summary

Confidence: low

Recommendation evidence is currently too limited for a maintained-repo choice. Use Implementation Status and Reproduction Path for a practical baseline plan.

Reproduction Risks

Estimate is based on paper-only reproduction flow

Hardware Notes

Expect multi-day setup/compute for meaningful reproduction based on current guidance.

Evidence disclosure

Evidence graph: 2 refs, 1 links.

Utility signals: depth 65/100, grounding 58/100, status medium.

Implementation Status

No verified maintained repo

There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.

No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
Start from related paper: Invariants based blur classification algorithm.
Track assumptions and missing details in an experiment log before coding.

Time to first repro: a few days

Reproduction readiness

No Repo

Time to first repro: days

Last checked: Jun 18, 2026

Hardware requirements

Expect multi-day setup/compute for meaningful reproduction based on current guidance.

No verified implementation available

· No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.

No benchmark numbers could be verified. You will not be able to validate reproduction correctness against published numbers.

Hugging Face artifacts

No trustworthy direct or curated related Hugging Face artifacts were found yet.

Continue with targeted Hugging Face searches derived from the paper title and method context:

Models

Calibration Artificial intelligence Calibration model

Datasets

Calibration dataset

Spaces

Calibration demo

Tip: start with models, then check datasets/spaces if you need evaluation data or demos.

Direct artifact matches are currently sparse. Use targeted Hugging Face searches to quickly locate candidate models, datasets, and demos.

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Research context

Citations

References

Tasks

Computer science, Calibration, Focus (optics), Motion blur, Camera resectioning, Lens (geology), Depth of field, Gaussian blur

Methods

None detected

Domains

Artificial intelligence, Computer vision, Image (mathematics)

Evaluation & Human Feedback Data

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