The Statistics of Shape, Reflectance, and Lighting in Real-World Scenes

2D images are highly ambiguous representations of 3D scenes, and this poses a fundamental obstacle to recovering shape and reflectance from shaded images. A Bayesian approach to overcoming this problem is to exploit statistical regularities in surface shapes, reflectances, and lighting conditions in real world scenes, in order to choose the most likely 3D interpretation of a 2D image. Here I review recent work on the statistical regularities in real world 3D scenes that biological or artificial visual systems could use to overcome image ambiguity, and psychophysical work on the assumptions that the human visual system relies on in order to perceive 3D scenes from 2D images.

[1]  E. Adelson,et al.  Image statistics and the perception of surface qualities , 2007, Nature.

[2]  Mark A. Georgeson,et al.  Sun and sky: Does human vision assume a mixture of point and diffuse illumination when interpreting shape-from-shading? , 2011, Vision Research.

[3]  P. Perona,et al.  Where is the sun? , 1998, Nature Neuroscience.

[4]  Alexander A. Mury,et al.  Structure of light fields in natural scenes. , 2009, Applied optics.

[5]  M. Gazzaniga,et al.  The new cognitive neurosciences , 2000 .

[6]  A. Gilchrist.  Seeing in Black and White , 2006 .

[7]  Bernice E. Rogowitz,et al.  Human Vision and Electronic Imaging II , 1997 .

[8]  William T. Freeman,et al.  Learning Low-Level Vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[9]  A. Gilchrist,et al.  An anchoring theory of lightness perception. , 1999 .

[10]  Richard F Murray,et al.  The human visual system's assumption that light comes from above is weak , 2011, Proceedings of the National Academy of Sciences.

[11]  Wendy J Adams,et al.  A common light-prior for visual search, shape, and reflectance judgments. , 2007, Journal of vision.

[12]  Katsumi Aoki,et al.  Recent development of flow visualization , 2004, J. Vis..

[13]  H. Bülthoff,et al.  Estimation of 3D shape from image orientations , 2011, Proceedings of the National Academy of Sciences.

[14]  Richard F. Murray.  Human lightness perception is guided by simple assumptions about reflectance and lighting , 2013, Electronic Imaging.

[15]  L. Maloney,et al.  The effect of perceived surface orientation on perceived surface albedo in binocularly viewed scenes. , 2003, Journal of vision.

[16]  Jitendra Malik,et al.  Shape, albedo, and illumination from a single image of an unknown object , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Jitendra Malik,et al.  High-frequency shape and albedo from shading using natural image statistics , 2011, CVPR 2011.

[18]  David J. Kriegman,et al.  The Bas-Relief Ambiguity , 2004, International Journal of Computer Vision.

[19]  Alan L. Yuille,et al.  The Generic Viewpoint Assumption and Planar Bias , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Richard F Murray,et al.  Human vision is attuned to the diffuseness of natural light. , 2014, Journal of vision.

[21]  Alexander A. Mury,et al.  Light field constancy within natural scenes. , 2007, Applied optics.

[22]  M. Ernst,et al.  Experience can change the 'light-from-above' prior , 2004, Nature Neuroscience.

[23]  P. Hanrahan,et al.  On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[24]  José R. A. Torreão.  Bayesian shape estimation: Shape-from-shading and photometric stereo revisited , 2005, Machine Vision and Applications.

[25]  Thomas B. Moeslund,et al.  Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  David Attewell,et al.  The distribution of reflectances within the visual environment , 2007, Vision Research.

[27]  Alexander A. Mury,et al.  Representing the light field in finite three-dimensional spaces from sparse discrete samples. , 2009, Applied optics.

[28]  Ron O Dror,et al.  Statistical characterization of real-world illumination. , 2004, Journal of vision.

[29]  Ronen Basri,et al.  Lambertian Reflectance and Linear Subspaces , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Barton L Anderson,et al.  Image statistics do not explain the perception of gloss and lightness. , 2009, Journal of vision.

[31]  Bui Tuong Phong.  Illumination for computer generated pictures , 1975, Commun. ACM.

[32]  Robin E. Hauck,et al.  An equivalent illuminant model for the effect of surface slant on perceived lightness. , 2004, Journal of vision.

[33]  Roland W Fleming,et al.  Real-world illumination and the perception of surface reflectance properties. , 2003, Journal of vision.

[34]  William T. Freeman,et al.  The generic viewpoint assumption in a framework for visual perception , 1994, Nature.

[35]  Paul Debevec.  Rendering synthetic objects into real scenes: bridging traditional and image-based graphics with global illumination and high dynamic range photography , 2008, SIGGRAPH Classes.

[36]  J. Todd.  Review TRENDS in Cognitive Sciences Vol.8 No.3 March 2004 The visual perception of 3D shape q , 2022 .