An object's material properties provide motion cues to three-dimensional shape

Perceiving the shape of three-dimensional objects is essential for interacting with them in daily life. If objects are constructed from different materials, can the human visual system accurately estimate their three-dimensional shape? We varied the thickness, motion, opacity, and specularity of globally convex objects rendered in a photorealistic environment. These objects were presented under either dynamic or static viewing condition. Observers rated the overall convexity of these objects along the depth axis. Our results show that observers perceived solid transparent objects as flatter than the same objects rendered with opaque reflectance properties. Regional variation in local root-mean-square image contrast was shown to provide information that is predictive of perceived surface convexity.

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Material properties derived from three-dimensional shape representations

Vision Research ◽

10.1016/j.visres.2015.05.003 ◽

2015 ◽

Vol 115 ◽

pp. 199-208 ◽

Cited By ~ 24

Author(s):

Phillip J. Marlow ◽

Barton L. Anderson

Keyword(s):

Material Properties ◽

Three Dimensional ◽

Shape Representations ◽

Dimensional Shape ◽

Three Dimensional Shape

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Computational studies of the extraction of visual spatial information from binocular and motion cues

Canadian Journal of Physiology and Pharmacology ◽

10.1139/y88-076 ◽

1988 ◽

Vol 66 (4) ◽

pp. 464-477 ◽

Cited By ~ 2

Author(s):

Ellen C. Hildreth

Keyword(s):

Computational Models ◽

Spatial Information ◽

Experimental Studies ◽

Three Dimensional ◽

Stereo Correspondence ◽

Motion Cues ◽

Visual Spatial ◽

Binocular Stereo ◽

Dimensional Shape ◽

Three Dimensional Shape

This paper reviews some of the contributions that work in computational vision has made to the study of biological vision systems. We concentrate on two areas where there has been strong interaction between computational and experimental studies: the use of binocular stereo to recover the distances to surfaces in space, and the recovery of the three-dimensional shape of objects from relative motion in the image. With regard to stereo, we consider models proposed for solving the stereo correspondence problem, focussing on the way in which physical properties of the world constrain possible methods of solution. We also show how critical observations regarding human stereo vision have helped to shape these models. With regard to the recovery of structure from motion, we focus on how the constraint of object rigidity has been used in computational models of this process.

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A Three-dimensional Shape Measurement Method Requiring only a Single Observation Image Based on Pattern Projection Technique

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.131.2224 ◽

2011 ◽

Vol 131 (12) ◽

pp. 2224-2225

Author(s):

Ke Sun ◽

Cunwei Lu

Keyword(s):

Measurement Method ◽

Three Dimensional ◽

Shape Measurement ◽

Projection Technique ◽

Single Observation ◽

Pattern Projection ◽

Dimensional Shape ◽

Three Dimensional Shape

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Four Dimensions of Diamond T: Combination Trade Marks of Colours And Shapes

Victoria University of Wellington Law Review ◽

10.26686/vuwlr.v37i4.5584 ◽

2006 ◽

Vol 37 (4) ◽

pp. 583

Author(s):

Michael McGowan

Keyword(s):

Three Dimensional ◽

Trade Mark ◽

Fourth Dimension ◽

Trade Marks ◽

Four Dimensions ◽

Trade Mark Law ◽

Dimensional Shape ◽

Three Dimensional Shape

This article examines the relatively new fields of colour and shape trade marks. It was initially feared by some academics that the new marks would encroach on the realms of patent and copyright. However, the traditional requirements of trade mark law, such as functionality and descriptiveness, have meant that trade marks in colour and shape are extremely hard to acquire if they do not have factual distinctiveness. As colour and shape trade marks have no special restrictions, it is proposed that the combination trade mark theory and analysis from the Diamond T case should be used as a way to make them more accessible. The combination analysis can be easily applied because every product has a three dimensional shape and a fourth dimension of colour.

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