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Updated: May 31, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

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Published on: November 14, 2019

Three-dimensional shape cues affect human and artificial recognition systems differently.

Mikayla Cutler1, Luke Baumel1, Joseph Tocco1

  • 1Department of Psychology, Loyola University Chicago, Chicago Illinois, United States of America.

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Summary
This summary is machine-generated.

Humans and neural networks process visual information differently. While humans prioritize shape, neural networks favor texture, but 3D cues can reduce this texture bias in networks.

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Area of Science:

  • Computer Vision
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Humans and artificial neural networks exhibit distinct biases in object recognition, with humans emphasizing shape and networks leaning towards texture.
  • Previous research often focused on external contours, overlooking internal cues like shading and shadows for shape definition.

Purpose of the Study:

  • To investigate how humans and neural networks utilize shape versus texture information, particularly when 3D cues (shading, shadows) are present.
  • To compare the performance of humans and networks on object classification tasks using texture-substituted images from various viewpoints.

Main Methods:

  • Generated 120,000 texture-substituted object images from ShapeNet models, varying viewpoints and including/excluding shading and attached shadows.
  • Tested human and neural network performance in classifying objects by shape and texture in these generated datasets.

Main Results:

  • Humans outperformed neural networks in classifying objects by shape, especially when 3D cues were included.
  • Neural networks' texture bias decreased with the inclusion of 3D cues.
  • 3D cues benefited human recognition for noncanonical views, while networks showed greater gains for canonical views.

Conclusions:

  • Fundamental differences exist in how humans and neural networks interpret 3D cues like shading and shadows for object recognition.
  • Humans appear to use these cues to infer 3D structure, whereas networks treat them as surface-level information akin to texture.