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Rendering synthetic objects into real scenes: bridging traditional and image-based graphics with global illumination and high dynamic range photography

Debevec, Paul·2008·ACM SIGGRAPH 2008 classes

Abstract

We present a method that uses measured scene radiance and global illumination in order to add new objects to light-based models with correct lighting. The method uses a high dynamic range image-based model of the scene, rather than synthetic light sources, to illuminate the new objects. To compute the illumination, the scene is considered as three components: the distant scene, the local scene, and the synthetic objects. The distant scene is assumed to be photometrically unaffected by the objects, obviating the need for reflectance model information. The local scene is endowed with estimated reflectance model information so that it can catch shadows and receive reflected light from the new objects. Renderings are created with a standard global illumination method by simulating the interaction of light amongst the three components. A differential rendering technique allows for good results to be obtained when only an estimate of the local scene reflectance properties is known.

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Metadata

Authors
Debevec, P.
Venue
ACM SIGGRAPH 2008 classes
Publisher
ACM
Published
2008-08-11
Pages
1-10
OpenAlex
W2003537776
Cited by
969
References
0
FWCI
74.29
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Rendering synthetic objects into real scenes: bridging traditional and image-based graphics with global illumination and high dynamic range photography. https://app.cochat.ai/papers/10.1145/1401132.1401175
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Debevec, P. (2008). Rendering synthetic objects into real scenes: bridging traditional and image-based graphics with global illumination and high dynamic range photography. ACM SIGGRAPH 2008 classes. https://doi.org/10.1145/1401132.1401175

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.