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Related Experiment Video

Updated: May 29, 2026

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

Shading, a view from the inside.

Jan J Koenderink¹, Andrea van Doorn, Sylvia Pont

¹EEMCS, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.

Seeing and Perceiving

|September 10, 2011

Summary

This summary is machine-generated.

This study introduces a novel first-person theory of shape from shading, moving beyond traditional Euclidean geometry. This new perspective offers robust, local shape inferences crucial for biological vision and artificial intelligence (AI).

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Area of Science:

Computer Vision
Psychology
Artificial Intelligence (AI)

Background:

Shape from shading, originating in art and psychology, was formalized by computer vision using Euclidean geometry and radiometry.
Existing formalisms are ill-suited for first-person perception, limiting psychological and biological relevance.
Previous attempts to apply computer vision models to psychology yielded limited success.

Purpose of the Study:

To propose a formal theory of the shading cue from a first-person perspective ('a view from the inside').
To develop a formalism relevant for biological systems and artificial intelligence (AI) applications.
To enable robust, local shape inferences free from calibration issues.

Main Methods:

Developed a new formalism based on the structure of pictorial and brightness spaces, not Euclidean geometry or radiometry.
Focused on a first-person, 'inside' view of the shading cue.
Considered predictions derived from the new theory and compared them with empirical evidence.

Main Results:

The proposed first-person formalism provides a biologically plausible account of shape from shading.
The theory demonstrates robustness and independence from calibration issues.
It facilitates purely local shape inferences, suitable for AI and biological implementation.

Conclusions:

A first-person formal theory of shape from shading is essential for understanding biological vision and advancing AI.
This new perspective overcomes limitations of traditional Euclidean and radiometric approaches.
The theory's predictions align with available empirical data, supporting its validity.