2022
DOI: 10.1098/rstb.2021.0443
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New Approaches to 3D Vision

Abstract: New approaches to 3D vision are enabling new advances in artificial intelligence and autonomous vehicles, a better understanding of how animals navigate the 3D world, and new insights into human perception in virtual and augmented reality. Whilst traditional approaches to 3D vision in computer vision (SLAM: simultaneous localization and mapping), animal navigation (cognitive maps), and human vision (optimal cue integration) start from the assumption that the aim of 3D vision is to provide an accurate 3D model … Show more

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Cited by 5 publications
(4 citation statements)
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“…Considerable evidence suggests that 3D vision is not a metric 3D reconstruction; some models of 3D space involve non-Euclid visual spaces (Linton et al, 2022). For example, Erkelens (2021) proposes a projective model of visual space perception that, when applied to pictorial space, predicts how regular structures are spaced in paintings (Erkelens, 2016).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Considerable evidence suggests that 3D vision is not a metric 3D reconstruction; some models of 3D space involve non-Euclid visual spaces (Linton et al, 2022). For example, Erkelens (2021) proposes a projective model of visual space perception that, when applied to pictorial space, predicts how regular structures are spaced in paintings (Erkelens, 2016).…”
Section: Discussionmentioning
confidence: 99%
“…However, if human vision aimed to infer a veridical 3D scene from every picture, then many artistic styles might seem incomprehensible when they are ambiguous, inconsistent, or do not follow any apparent strict projection rules. In fact, recent theories of 3D vision suggest that the visual system does not aim to infer coherent 3D shape from the real world (Linton et al, 2022), and so we would not expect picture perception to do so either.…”
Section: Introductionmentioning
confidence: 95%
“…This suggests a very different approach to perceptual learning than currently employed in deep neural networks ( figure 13 ) (for a review, see the Introduction article to this issue [ 86 ]). On my account the input to perceptual learning would be depth from disparity (so something more than the 2D image input in [ 23 ]), but we would still have to learn the concept of 3D space as a space in which we move, and which contains 3D objects that persist across time (so something less than the innate 'geometry awareness' of [ 79 ]).…”
Section: Two-stage Theory Of Visual Processingmentioning
confidence: 99%
“…Our visual perceptions begin with a flat two-dimensional (2D) image from which we must extract the embedded third dimension (see [ 1 ] for a recent review and [ 2 ] for a recent proposal). We are readily able to judge the scale (the physical size of the grain) of novel environments and the depth relations within them, even if they don’t contain familiar objects (e.g.…”
Section: Introductionmentioning
confidence: 99%