How machines perceive visual illusions is essential to clarify the differences between artificial and human perception. Crater illusions cause depth perception in the texture by the intensity gradient of the disks. In this study, we investigated whether a monocular depth estimator perceives the crater illusions. We adopted MonoDepth as the estimator and conducted two experiments using the illusions with planar images and attached to a cube. As a result, the estimator perceived the convexity on the top-lit disk, while a little concavity on the bottom-lit disk. We also found the vertical heterogeneity for the depth perception of it. Additionally, the machine perceived the illusion attached to the cube, although the indeterminacy for object scale was revealed. Our findings would bridge the research between computer vision and cognitive science in terms of depth perception.

Issue Section:
Special Session: Artificial Perception: Machines with Lifelike Failings
depth perception, crater illusion, unsupervised monocular depth estimation, convolutional neural network
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© 2021 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
2021
Massachusetts Institute of Technology
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Issue Section:
Special Session: Artificial Perception: Machines with Lifelike Failings