Evidence map›Paper›PMID 42161917›Full record

ArticleNature communications2026

Accurate and fast event-based shape measurement of mixed reflectance scenes.

Aniket Dashpute, Jiazhang Wang, James Taylor, Oliver Cossairt, Ashok Veeraraghavan, Florian Willomitzer

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Aniket DashputeDepartment of Electrical and Computer Engineering, Rice University, Houston, TX, USA.ORCID http://orcid.org/0000-0001-8201-1405
Jiazhang WangWyant College of Optical Sciences, University of Arizona, Tucson, AZ, USA. jiazhangwang@arizona.edu.ORCID http://orcid.org/0009-0008-5657-5483
James TaylorWyant College of Optical Sciences, University of Arizona, Tucson, AZ, USA.ORCID http://orcid.org/0000-0001-8483-9118
Oliver CossairtDepartment of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA.
Ashok VeeraraghavanDepartment of Electrical and Computer Engineering, Rice University, Houston, TX, USA.ORCID http://orcid.org/0000-0001-5043-7460
Florian WillomitzerWyant College of Optical Sciences, University of Arizona, Tucson, AZ, USA.ORCID http://orcid.org/0000-0003-1563-1131

Funding

National Science Foundation (NSF) 2153516National Science Foundation (NSF) 2328619United States Department of Defense | Defense Advanced Research Projects Agency (DARPA) HR0011-24-30347
6 · The paper itself

Abstract

Event-based structured light systems have recently emerged as an exciting alternative to frame-based triangulation for 3D measurement of diffuse surfaces, offering high dynamic range and fast capture speed, but at the cost of reduced data quality. Existing event-based and frame-based 3D imaging systems are typically tailored to a single surface reflectance type, diffuse or specular, and therefore struggle with mixed reflectance scenes. In this work, we present an event-based structured light system for fast and accurate 3D imaging of mixed reflectance scenes. Using epipolar constraints on the captured events, we decompose reflections into diffuse, two-bounce specular, and other multi-bounce reflections. Diffuse surfaces are reconstructed via triangulation and repurposed as a virtual screen for deflectometry to evaluate specular surfaces, requiring only a scanning laser and an event camera. Our system achieves motion-robust 3D reconstructions at  < 600 μm depth accuracy and introduces a fast diffuse-only capture mode operating at 250 Hz.

Identifiers

PMID42161917
PMCPMC13190685

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.