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Rich Intrinsic Image Decomposition of Outdoor Scenes from Multiple Views.

Pierre-Yves Laffont, Adrien Bousseau, George Drettakis

IEEE Transactions on Visualization and Computer Graphics

|April 18, 2012

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This study introduces a novel method for estimating intrinsic images from outdoor scenes using multiple camera views. The technique accurately separates reflectance and illumination, enabling advanced image manipulation.

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Area of Science:

Computer Vision
Computer Graphics
Image Processing

Background:

Intrinsic image decomposition is crucial for image analysis and manipulation.
Existing methods often require precise geometry or manual input, limiting their applicability.
Ambiguities in separating reflectance and illumination pose a significant challenge.

Purpose of the Study:

To develop a method for estimating intrinsic images from multiple views of outdoor scenes.
To overcome the limitations of existing methods by reducing reliance on precise geometry.
To enable detailed illumination decomposition for enhanced image manipulation.

Main Methods:

Utilized multiview stereo to reconstruct a sparse 3D point cloud from multiple images.
Developed an optimization algorithm to estimate sun visibility and compensate for incomplete geometry.
Employed image-guided propagation to transfer 3D information to 2D image pixels.

Main Results:

Successfully estimated intrinsic images, decomposing illumination into sun, sky, and indirect components.
Achieved accurate shadow extraction despite sparse and incomplete 3D data.
Demonstrated novel image manipulation capabilities enabled by the rich illumination decomposition.

Conclusions:

The proposed method effectively estimates intrinsic images from multiview outdoor scenes without precise geometry.
The technique provides a detailed decomposition of illumination, enhancing image analysis and manipulation possibilities.
This approach offers a robust solution for intrinsic image decomposition in real-world scenarios.