ArticleMethodsX2026
Development of an augmented virtuality framework using user-defined passthrough surfaces.
Article in MethodsX, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
Who cites it
0 citing papers in PubMed.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An augmented virtuality framework is presented in which the physical scene is admitted only through developer-defined geometric apertures, while all other pixels are rendered virtually. The scene initialises as a black field and uses surface-projected, overlay-style compositing so that the camera stream is visible only on circular or square meshes registered as projection surfaces. Tracking origin is configured to minimise unintended recentring, and the same method can operate either as a minimal black scene or within an optional three-dimensional environment. Main features of the framework are as follows: deterministic compositing that binds real imagery to user-defined meshes and prevents leakage outside apertures. stable alignment between virtual geometry and the physical workspace during head motion achieved through standard XR configuration. a flexible scene recipe that supports circular or square apertures and optional contextual environments without altering projection logic. The framework is intended for sensory and consumer studies that require real product interaction under controlled context and generalises to training, human factors, and rehabilitation scenarios requiring constrained visibility of the real world.
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Registered trials
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.