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Title: On the Inverse Problem of Binocular 3D Motion Perception}
Article:
Abstract:
It is shown that existing processing schemes of 3D motion perception such as interocular velocity difference, changing disparity over time, as well as joint encoding of motion and disparity, do not offer a general solution to the inverse optics problem of local binocular 3D motion. Instead we suggest that local velocity constraints in combination with binocular disparity and other depth cues provide a more flexible framework for the solution of the inverse problem. In the context of the aperture problem we derive predictions from two plausible default strategies:( 1) the vector normal prefers slow motion in 3D whereas( 2) the cyclopean average is based on slow motion in 2D. Predicting perceived motion directions for ambiguous line motion provides an opportunity to distinguish between these strategies of 3D motion processing. Our theoretical results suggest that velocity constraints and disparity from feature tracking are needed to solve the inverse problem of 3D motion perception. It seems plausible that motion and disparity input is processed in parallel and integrated late in the visual processing hierarchy.
Introduction:
The representation of the three-dimensional( 3D) external world from two-dimensional( 2D) retinal input is a fundamental problem that the visual system has to solve[1]–[4]. This is true for static scenes in 3D as well as for dynamic events in 3D space. For the latter the inverse problem extends to the inference of dynamic events in a 3D world from 2D motion signals projected into the left and right eye. In the following we exclude observer movements and only consider passively observed motion. Velocity in 3D space is described by motion direction and speed. Motion direction can be measured in terms of azimuth and elevation angle, and motion direction together with speed is conveniently expressed as a 3D motion vector in a cartesian coordinate system. Estimating such a vector locally is highly desirable for a visual system because the representation of local estimates in a dense vector field provides the basis for the perception of 3D object motion, that is direction and speed of moving objects. This information is essential for interpreting events as well as planning and executing actions in a dynamic environment. If a single moving point, corner or other unique feature serves as binocular input then intersection of constraint lines or triangulation together with a starting point provides a straightforward and unique geometrical solution to the inverse problem in a binocular viewing geometry( see Methods and Fig. 1 for an illustration). If, however, the moving stimulus has spatial extent, such as an edge, contour, or line inside a circular aperture[5] then local motion direction in corresponding receptive fields of the left and right eye remains ambiguous and additional constraints are needed to solve the aperture and inverse problem in 3D. The inverse optics and the aperture problem are well-known problems in computational vision, especially in the context of stereo[3],[6], structure from motion[7], and optic flow[8]. Gradient constraint methods belong to the most widely used techniques of optic-flow computation from image sequences. They can be divided into local area-based[9] and into more global optic flow methods[10]. Both techniques employ brightness constancy and smoothness constraints in the image to estimate velocity in an over-determined equation system. It is important to note that optical flow only provides a constraint in the direction of the image gradient, the normal component of the optical flow. As a consequence some form of regularization or smoothing is needed. Similar techniques in terms of error minimization and regularization have been offered for 3D stereo-motion detection[11]–[13]. Essentially these algorithms extend processing principles of 2D optic flow to 3D scene flow. Computational studies on 3D motion algorithms are usually concerned with fast and efficient encoding when tested against ground truth. Here we are less concerned with the efficiency or robustness of a particular implementation. Instead we want to understand and predict behavioral characteristics of human 3D motion perception. 2D motion perception has been extensively researched in the context of the 2D aperture problem[14]–[16] but there is a surprising lack of studies on the aperture problem and 3D motion perception. Any physiologically plausible solution to the inverse 3D motion problem has to rely on binocular sampling of local spatio-temporal information. There are at least three known cell types in early visual cortex that may be involved in local encoding of 3D motion: simple and complex motion detecting cells[17]–[20], binocular disparity detecting cells[21] sampled over time, and joint motion and disparity detecting cells[22]–[24]. It is therefore not surprising that three approaches to binocular 3D motion perception have emerged in the literature: Interocular velocity difference( IOVD), changing disparity over time( CDOT), and joint encoding of motion and disparity( JEMD). These three approaches have generated an extensive body of research but psychophysical results have been inconclusive and the nature of 3D motion processing remains an unresolved issue[25],[26]. Despite the wealth of empirical studies on motion in depth there is a lack of studies on true 3D motion stimuli. Previous psychophysical and neurophysiological studies typically employ stimulus dots with unambiguous motion direction or fronto-parallel random-dot surfaces moving in depth. The aperture problem and local motion encoding however, which features so prominently in 2D motion perception[14]–[16] has been neglected in the study of 3D motion perception. Large and persistent perceptual bias has been found for dot stimuli with unambiguous motion direction[27]–[29] suggesting processing strategies that are different from the three main processing models[28]–[30]. It seems promising to investigate local motion stimuli with ambiguous motion direction such as a line or contour moving inside a circular aperture[31] because they relate to local encoding[17]–[24] and may reveal principles of 3D motion processing[32]. The aim of this paper is to evaluate existing models of 3D motion perception and to gain a better understanding of binocular 3D motion perception. First, we show that existing models of 3D motion perception are insufficient to solve the inverse problem of binocular 3D motion. Second, we establish velocity constraints in a binocular viewing geometry and demonstrate that additional information is necessary to disambiguate local velocity constraints and to derive a velocity estimate. Third, we compare two default strategies of perceived 3D motion when local motion direction is ambiguous. It is shown that critical stimulus conditions exist that can help to determine whether 3D motion perception favors slow 3D motion or averaged cyclopean motion.
Discussion:
IOVD and CDOT are extreme models because they are based on either motion or disparity input. IOVD excludes contributions from binocular disparity processing but requires early stereo correspondence. It does not solve the inverse problem for local 3D line motion because it is confined to 3D motion in the x- or z-plane. CDOT on the other hand excludes contributions from motion processing and therefore has problems to establish motion correspondence and direction. Without further assumptions it is confined to motion in depth along the line of sight. If either motion or disparity input determines 3D motion perception then processing of any additional input needs to be disengaged or silenced. Instead, the visual system may take advantage of motion and disparity input[59],[60] as well as additional cues. Here we favor parallel processing and late integration over early joint encoding because the inverse problem for local 3D motion remains ill posed for JEMD and a population read-out needs to be specified to approximate global 3D motion at a later stage. Combining global disparity or depth information with local velocity constraints at a later stage solves the inverse problem of local 3D motion and provides a flexible scheme that can exploit intermediate depth processing such as relative and orientation disparity in V2 and V4[44],[61]. Velocity constraints may be processed in the ventral stream and binocular disparity together with other depth cues in the dorsal stream[62]. It seems anatomically and neurophysiologically plausible that integration of motion and disparity occurs late in subregions of human V5/MT[55],[63]–[65] if not in areas beyond V5/MT[66]. What enables the visual system to instantaneously perceive 3D motion and to infer direction and speed of a moving object? It seems likely that the visual system exploits many cues to make this difficult inference as reliable and veridical as possible and the diverse set of effective local and global cues in psychophysical studies[59],[67] already points at late integration within the visual processing hierarchy[62],[65],[66]. More specifically, we suggest that binocular 3D motion perception may be based on parallel motion and depth processing. Thereby motion processing captures local spatio-temporal constraints in the scene whereas depth processing provides a global and dynamic depth map that helps to disambiguate motion direction and to maintain a detailed spatial representation of the scene. Late integration of motion and disparity constraints in combination with other cues can solve the inverse problem of local 3D motion and allows the visual system to remain flexible when binding and segmenting local inputs from different processing stages into a global 3D motion percept. Parallel processing and late integration may explain why, compared to 2D motion perception, 3D motion perception shows reduced spatio-temporal tuning characteristics[68],[69] and why motion perception can retain relatively fine spatial detail. The combination of local motion constraints with a global dynamic depth map from higher-order features would also explain the perception of different types of non-linear motion, such as non-rigid and 2nd order motion. The notion of parallel pathways feeding functionally different aspects of motion perception into a later stage is not new and has been advanced in the context of 2D motion direction and speed perception[70],[71], 2D pattern motion[15],[56],[58], eye movements[72],[73], and the processing of higher order motion[74],[75] but was not often addressed in the context of binocular 3D motion perception[75],[76]. Considering the ill-posed inverse problem of existing approaches and the under-determined characteristics of local binocular motion constraints, parallel processing and late integration of motion and disparity as well as other cues appears particularly convincing because solving the inverse problem for local 3D motion adds a functional significant aspect to the notion of parallel streams of dynamic disparity and motion processing. It will require considerable efforts to unravel the entire process but recent developments in the framework of Bayesian inference[28],[29],[56] look promising to extend the geometric considerations given here.
Humans and many other predators have two eyes that are set a short distance apart so that an extensive region of the world is seen simultaneously by both eyes from slightly different points of view . Although the images of the world are essentially two-dimensional , we vividly see the world as three-dimensional . This is true for static as well as dynamic images . Here we elaborate on how the visual system may establish 3D motion perception from local input in the left and right eye . Using tools from analytic geometry we show that existing 3D motion models offer no general solution to the inverse optics problem of 3D motion perception . We suggest a flexible framework of motion and depth processing and suggest default strategies for local 3D motion estimation . Our results on the aperture and inverse problem of 3D motion are likely to stimulate computational , behavioral , and neuroscientific studies because they address the fundamental issue of how 3D motion is represented in the visual system .