Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalization of the Lambertian model and implications for machine vision
International Journal of Computer Vision
Computer graphics (2nd ed. in C): principles and practice
Computer graphics (2nd ed. in C): principles and practice
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape-from-Shading Under Perspective Projection
International Journal of Computer Vision
Shape from Shading: A Well-Posed Problem?
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A New Formulation for Shape from Shading for Non-Lambertian Surfaces
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Unifying and Rigorous Shape from Shading Method Adapted to Realistic Data and Applications
Journal of Mathematical Imaging and Vision
Facial Shape-from-shading and Recognition Using Principal Geodesic Analysis and Robust Statistics
International Journal of Computer Vision
3-D Reconstruction of Shaded Objects from Multiple Images Under Unknown Illumination
International Journal of Computer Vision
Perspective Shape from Shading with Non-Lambertian Reflectance
Proceedings of the 30th DAGM symposium on Pattern Recognition
Some remarks on perspective shape-from-shading models
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Vector-valued image regularization with PDE's: a common framework for different applications
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Image Processing
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Although shape from shading (SfS) has been studied for almost four decades, the performance of most methods applied to real-world images is still unsatisfactory: This is often caused by oversimplified reflectance and projection models as well as by ignoring light attenuation and nonconstant albedo behavior. We address this problem by proposing a novel approach that combines three powerful concepts: (i) By means of a Chan-Vese segmentation step, we partition the image into regions with homogeneous reflectance properties. (ii) This homogeneity is further improved by an adaptive thresholding that singles out unreliable details which cause fluctuating albedos. Using an inpainting method based on edge-enhancing anisotropic diffusion, structures are filled in such that the albedo does no longer suffer from fluctuations. (iii) Finally a sophisticated SfS method is used that features a perspective projection model, considers physical light attenuation and models specular highlights. In our experiments we demonstrate that each of these ingredients improves the reconstruction quality significantly. Their combination within a single method gives favorable perfomance also for images that are taken under real-world conditions where simpler approaches fail.