ArticleLight, science & applications2026
Single-view neural illumination estimation and editing for dynamic light field display.
Article in Light, science & applications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
10 authors.
Funding
Abstract
Light field rendering is widely applied to virtual reality (VR), augmented reality (AR), mixed reality (MR) and extended reality (XR). For photorealistic light field displays, it requires a dense view sampling of the scene. However, in dynamic immersive interactions, the available observations are often too sparse to synthesize the complete light field required for a high-fidelity display. Therefore, it poses a huge challenge for generating photometrically consistent views between the virtual and real world. Here, we introduce a neural illumination estimation and editing framework for adaptive light field synthesis. The proposed method can explicitly encode intrinsic parameters of illumination from one single sampling view, which is used for a hybrid-guided generative network to synthesize photometrically plausible dense views of the scene under the guidance of a complete rendering model. It deconstructs the baked-in lighting to enable consistent and high-fidelity relighting from any viewpoint. Our method estimates and edits illumination with only 0.2397 W m
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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.