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New Approaches to 3D Vision.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2022

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The case against probabilistic inference: a new deterministic theory of 3D visual processing.

Fulvio Domini¹

¹CLPS, Brown University, 190 Thayer Street Providence, Rhode Island 02912-9067, USA.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences

|December 13, 2022

Summary

The brain

Keywords:

3D vision computational models cue integration depth perception

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Area of Science:

Neuroscience
Computational Vision
Perception

Background:

Human vision fundamentally addresses 3D information from ambiguous visual input.
Probabilistic inference, particularly maximum-likelihood estimation (MLE), dominates current models of 3D vision.
Existing MLE models assume independent depth-cue modules with weighted averaging.

Purpose of the Study:

To challenge the prevailing maximum-likelihood estimation (MLE) model in 3D vision.
To propose a new theoretical framework, Intrinsic Constraint, for understanding 3D visual perception.
To introduce the Vector Sum model as an alternative to MLE.

Main Methods:

Critically evaluating psychophysical findings against the MLE model.
Demonstrating the limitations of MLE in explaining perceptual uncertainty and just noticeable differences.
Developing and presenting the Intrinsic Constraint theory and Vector Sum model.

Main Results:

The MLE model fails to account for key psychophysical data and misinterprets measures of stimulus discriminability.
The proposed Intrinsic Constraint theory offers a more robust explanation for 3D visual perception.
The Vector Sum model successfully explains existing findings and predicts new ones contradicting MLE.

Conclusions:

The visual system prioritizes stable interpretations over the most probable ones.
The Vector Sum model, under the Intrinsic Constraint theory, provides a better account of 3D visual processing.
This work necessitates a re-evaluation of current models in 3D vision research.