Pith. Sign in

RECORD

Unsupervised Learning Based Focal Stack Camera Depth Estimation

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Zhengyu Huang, Weizhi Du, Theodore B. Norris

Image and Video Processing · Submitted 14 Mar 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim On the NYU-v2 dataset, our method achieves much better depth estimation accuracy compared to single-image based methods.

how it is stated · asserted 1.00 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID2203.07904 (version 3)
Pith IDAAKWKLUU, Pith's permanent record of this paper and its review
CitationTheodore B. Norris, Weizhi Du, Zhengyu Huang. "Unsupervised Learning Based Focal Stack Camera Depth Estimation." arXiv:2203.07904 (2022). https://pith.science/paper/AAKWKLUU
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,213 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.05 · human participants 0.04 · controlled comparison 0.85 · randomized assignment 0.01 · states quantitative result 0.07 · compares with prior results 0.96 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; .

We propose an unsupervised deep learning based method to estimate depth from focal stack camera images. On the NYU-v2 dataset, our method achieves much better depth estimation accuracy compared to single-image based methods.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We propose an unsupervised deep learning based method to estimate depth from focal stack camera images. contribution 0.97 0.81 0.13 asserted 0.98
s1 On the NYU-v2 dataset, our method achieves much better depth estimation accuracy compared to single-image based methods. result 1.00 0.95 0.65 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.75.

What was examined, and what was not

sources read
  • arXiv listing abstract v3
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We propose an unsupervised deep learning based method to estimate depth from focal stack camera images. On the NYU-v2 dataset, our method achieves much better depth estimation accuracy compared to single-image based methods.

Human review

How Pith checks papers → · Full audit of this paper →

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.