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Automatic Scene Inference for 3D Object Compositing

delete2014-06-02
delete123
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OA
AI
K
Kalyan Sunkavalli
N
Nathan Carr
H
Hailin Jin
D
David Forsyth
DOI:10.1145/2602146delete
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Abstract

Abstract

En 中文
We present a user-friendly image editing system that supports a drag-and-drop object insertion (where the user merely drags objects into the image, and the system automatically places them in 3D and relights them appropriately), postprocess illumination editing, and depth-of-field manipulation. Underlying our system is a fully automatic technique for recovering a comprehensive 3D scene model (geometry, illumination, diffuse albedo, and camera parameters) from a single, low dynamic range photograph. This is made possible by two novel contributions: an illumination inference algorithm that recovers a full lighting model of the scene (including light sources that are not directly visible in the photograph), and a depth estimation algorithm that combines data-driven depth transfer with geometric reasoning about the scene layout. A user study shows that our system produces perceptually convincing results, and achieves the same level of realism as techniques that require significant user interaction.
Keywords:
Algorithms
Human Factors
Illumination inference
depth estimation
scene reconstruction
physically grounded
image-based rendering
image-based editing
AI Summary

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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

ACM Transactions on Graphics cover
ACM Transactions on Graphics
IF:
9.5
Papers:
4.8K
Citations:
3.6W

Organization

U
University of Illinois Chicago
Scholars:
1.7W
Papers: 1.4W
Citations: 3.0W
University of Illinois System cover
University of Illinois System
Scholars:
6.9W
Papers: 6.2W
Citations: 644
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