Bayesian models of binocular 3-D motion perception

Lages, Martin

doi:10.1167/6.4.14

Cited by 36 publications

(60 citation statements)

References 34 publications

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“…Motion and disparity processing informs Bayesian 3D motion estimation Welchman et al (1) propose a Bayesian model that combines a velocity prior for slow motion (2, 3) with approximations of lateral velocity V x and velocity in depth V z to model biased perception of 3D motion trajectories in the x-z plane (3,4). Although decomposing a motion vector into orthogonal components may be mathematically convenient it raises the question of why the visual system should approximate these velocities in the first place.…”

Section: Lettermentioning

confidence: 99%

Motion and disparity processing informs Bayesian 3D motion estimation

Lages¹,

Heron²

2008

Proc. Natl. Acad. Sci. U.S.A.

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Welchman et al.(1) propose a Bayesian model that combines a velocity prior for slow motion (2, 3) with approximations of lateral velocity V x and velocity in depth V z to model biased perception of 3D motion trajectories in the x-z plane (3, 4). Although decomposing a motion vector into orthogonal components may be mathematically convenient it raises the question of why the visual system should approximate these velocities in the first place.The binocular visual system has no immediate mechanism to encode retinal velocities in terms of component V x and V z . It must estimate both vectors through retinal motion and/or disparity input. (Note that interocular velocity difference and change of disparity are mathematically equivalent but may reflect physiologically different processing streams.) Retinal motion and/or disparity are readily available to the visual system (5), and these signals are sufficient to estimate motion trajectories in a binocular viewing geometry (3). Thus, a 3D trajectory can be estimated directly without first approximating vectors V x and V z and corresponding likelihoods.Lages (3) developed Bayesian models of binocular 3D motion perception that combine a prior for slow motion in x-z with likelihood constraints of motion and/or disparity processing to explain perceptual bias of motion trajectories. By comparing three model types (motion-first, stereo-first, and stereo-motion) it was concluded that disparity rather than motion processing introduces perceptual bias for 3D trajectories ranging over 360°. These Bayesian models have the advantage that likelihoods are based on physiologically plausible motion and/or disparity processing.

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Section: Lettermentioning

confidence: 99%

Motion and disparity processing informs Bayesian 3D motion estimation

Lages¹,

Heron²

2008

Proc. Natl. Acad. Sci. U.S.A.

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“…Indeed, previous work has reported systematic biases in the estimation of both real and virtual object motion in depth, such that objects appear to move more sideways (Harris & Dean, 2003;Welchman, Tuck, & Harris, 2004;Harris & Drga, 2005;Gray, Regan, Castaneda, & Sieffert, 2006;Poljac, Neggers, & van den Berg, 2006;Lages, 2006;Rushton & Duke, 2007;Welchman, Lam, & Bülthoff, 2008;Duke & Rushton, 2012). This lateral bias is thought to arise based on the geometry of 3D motion perception and the mechanism for 2D speed perception described above (Welchman et al, 2008).…”

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confidence: 97%

Sensory uncertainty leads to systematic misperception of the direction of motion in depth

Fulvio

Rosén

Rokers

2015

Atten Percept Psychophys

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Although we have made major advances in understanding motion perception based on the processing of lateral (2D) motion signals on computer displays, the majority of motion in the real (3D) world occurs outside of the plane of fixation, and motion directly toward or away from observers has particular behavioral relevance. Previous work has reported a systematic lateral bias in the perception of 3D motion, such that an object on a collision course with an observer's head is frequently judged to miss it, with obvious negative consequences. To better understand this bias, we systematically investigated the accuracy of 3D motion perception while manipulating sensory noise by varying the contrast of a moving target and its position in depth relative to fixation. Inconsistent with previous work, we found little bias under low sensory noise conditions. With increased sensory noise, however, we revealed a novel perceptual phenomenon: observers demonstrated a surprising tendency to confuse the direction of motion-in-depth, such that approaching objects were reported to be receding and vice versa. Subsequent analysis revealed that the lateral and motion-in-depth components of observers' reports are similarly affected, but that the effects on the motion-in-depth component (i.e., the motion-in-depth confusions) are much more apparent than those on the lateral component. In addition to revealing this novel visual phenomenon, these results shed new light on errors that can occur in motion perception and provide a basis for continued development of motion perception models. Finally, our findings suggest methods to evaluate the effectiveness of 3D visualization environments, such as 3D movies and virtual reality devices.

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“…The present approach extends existing probabilistic models [24,11] to 3D motion and provides a velocity estimate for the 3D aperture problem [12]. The underlying geometricstatistical model is based on monocular constraints in a binocular viewing geometry.…”

Section: Bayesian Models Of 3d Motion Perceptionmentioning

confidence: 99%

“…Similarly, Lages [11] introduced a bivariate Gaussian prior to explain bias in perceived trajectory and speed of a target moving on the horizontal plane. If we assume that most features and objects in a scene are stationary or tend to move slowly on an arbitrary trajectory in 3D space then an isotropic 3D Gaussian provides a plausible world prior for binocular 3D motion perception.…”

Section: Bayesian Motion (Bm) Modelmentioning

confidence: 99%

“…It is well known that observers underestimate slant about the x-axis. This may be modelled by estimation bias in an over-determined equation system [9] or by an explicit disparity prior in a probabilistic approach [11,18]. In an extension of the BM model we introduce disparity computation in the first processing stage.…”

Section: Bayesian Disparity Motion (Bdm) Modelmentioning

confidence: 99%

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A Bayesian approach to the aperture problem of 3D motion perception

Wang

Heron

Moreland

et al. 2012

2012 International Conference on 3D Imaging (IC3D)

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We suggest a geometric-statistical approach that can be applied to the 3D aperture problem of motion perception. In simulations and psychophysical experiments we study perceived 3D motion direction in a binocular viewing geometry by systematically varying 3D orientation of a line stimulus moving behind a circular aperture. Although motion direction is inherently ambiguous perceived directions show systematic trends and a Bayesian model with a prior for small depth followed by slow motion in 3D gives reasonable fits to individual data. We conclude that the visual system tries to minimize velocity in 3D but that earlier disparity processing strongly influences perceived 3D motion direction. We discuss implications for the integration of disparity and motion cues in the human visual system.

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Bayesian models of binocular 3-D motion perception

Cited by 36 publications

References 34 publications

Motion and disparity processing informs Bayesian 3D motion estimation

Motion and disparity processing informs Bayesian 3D motion estimation

Sensory uncertainty leads to systematic misperception of the direction of motion in depth

A Bayesian approach to the aperture problem of 3D motion perception

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