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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

Estimating changes in lighting direction in binocularly viewed three-dimensional scenes.

Holly E Gerhard¹, Laurence T Maloney

¹Department of Psychology, New York University, New York, NY, USA. hgerhard@gmail.com

Journal of Vision

|November 26, 2010

Summary

Human observers can detect changes in scene lighting direction. Errors in judging lighting changes are mainly due to difficulties in perceiving surface shape, impacting visual perception models.

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Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

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Published on: December 4, 2013

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Last Updated: Jun 6, 2026

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

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Published on: July 21, 2020

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Published on: December 4, 2013

Area of Science:

Computer Vision
Human Perception
Computational Neuroscience

Background:

Understanding how humans perceive 3D scenes and lighting is crucial for realistic computer graphics and artificial intelligence.
Previous research has explored shape-from-shading and lighting estimation, but direct links to human detection of lighting changes remain less understood.

Purpose of the Study:

To investigate human accuracy in detecting directional changes in scene lighting.
To model the relationship between surface shape, shading, and the perception of lighting shifts.
To identify specific surface features that influence performance in lighting change detection tasks.

Main Methods:

Thirteen observers viewed stereoscopic 3D rendered scenes with varying lighting conditions.
Scenes featured a 3D Gaussian bump surface illuminated by collimated and diffuse light sources.
Observers classified the direction of small, rapid changes in the collimated light source position.

Main Results:

All observers performed significantly above chance in detecting lighting direction changes.
A computational model combining shape and shading maps accurately predicted observer errors.
Errors in lighting direction estimation were strongly correlated with inaccuracies in perceived surface shape.

Conclusions:

Human visual system's ability to detect lighting changes is robust but susceptible to surface shape representation errors.
The developed model provides insights into the interplay of shape and shading in visual scene understanding.
Further research can refine models of visual perception by characterizing the impact of surface features on performance.