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.
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.
This influential processing model assumes that monocular spatio-temporal differentiation or motion detection [33] is followed by a difference computation between velocities in the left and right eye [34]–[36]. The difference or ratio between monocular motion vectors in each eye, usually in a viewing geometry where interocular separation i and viewing distance D is known, provides an estimate of motion direction in terms of azimuth angle only. We argue that the standard IOVD model [29], [37]–[40] is incomplete and ill-posed if we consider local motion encoding and the aperture problem. In the following the limitations of the IOVD model are illustrated. This alternative processing scheme uses disparity input and monitors changing disparity over time (CDOT). Disparity between the left and right image is detected [45] and changes over time give rise to motion-in-depth perception [46]–[49]. We argue that this approach also has limitations when the inverse problem of local 3D motion is considered. This approach postulates that early binocular cells are both motion and disparity selective and physiological evidence for the existence of such cells was found in cat striate cortex [22] and monkey V1 [50] (see however [51]). Model cells in this hybrid approach extract motion and disparity energy from local stimulation. A read-out from population activity and population decoding is needed to explain global 3D motion phenomena such as transparent motion and Pulfrich-like effects [52], [53]. Although JEMD is physiologically plausible it shares two problems with IOVD. Which constraints does the visual system use to solve the inverse as well as aperture problem for local 3D line motion where endpoints are invisible or occluded? This is a critical question because it is linked to local motion encoding and the possible contribution from depth processing. The 3D motion system may establish constraint planes rather than constraint lines to capture all possible motion directions of a contour or edge, including motion in the direction of the edge' s orientation. Geometrically the intersection of two constraint planes in a given binocular viewing geometry defines a constraint line oriented in 3D velocity space (see Fig. 3 and Methods). We suggest that in analogy to 2D motion perception [15], [56] tracking of features in depth coupled with binocular velocity constraints from motion processing provides a flexible strategy to disambiguate 3D motion direction and to solve the inverse problem of 3D motion perception. But which principles or constraints are used? Does the binocular motion system prefer slow 3D motion or averaged 2D motion? Does it solve stereo correspondence before establishing binocular velocity constraints or does it average 2D velocity constraints from the left and right eye before it solves stereo correspondence? We derive predictions for two alternative strategies to address these questions.
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.
If the eyes remain verged on a fixation point in a binocular viewing geometry then the constraint line in the left and right eye can be defined by pairs of points and, respectively. The nodal point in the left eye and a projection point of the motion vector on the left retina define a constraint line for the left eye. Similarly, points and determine a constraint line in the right eye. The corresponding vector directions are given by (1) Each constraint line can expressed by a pair of points and together with scalar t: (2) The two lines intersect for (3) if and only if (4) where is the scalar product also called the dot product, × denotes the cross product, and the norm of a vector. Otherwise, the two lines are skew, and the inverse problem is ill posed. We can exclude the trivial case because the two eyes are separated by. We also exclude the special case where the cross product is zero because the motion vectors in the left and right eye are identical or opposite. The cross product in (4) can be written as (5) Since in Eq. (4) we are only concerned with the product which equals zero if and only if (6) The ratio of z co-ordinates on the right-hand side may be different from 1 as a result of eye vergence and the left-hand side reflects the corresponding ratio of vertical displacements. In the following we consider the simpler case of projections onto a fronto-parallel screen (coplanar retinae) at a fixed viewing distance D (see Fig. 2). In this case epipolar lines are horizontal with equivalent co-ordinates on the z-axis. Again, since in (4) we only have to evaluate which is zero if and only if: (7) For an intersection to exist the left and right eye motion vector must have equivalent horizontal y co-ordinates or zero vertical disparity. Monocular line motion defines a constraint plane with three points: the nodal point of an eye and two points defining the end position of the projected line (see Fig. 3). In order to find the intersection of the left and right eye constraint plane we use the plane normal in the left and right eye. If the two planes are specified in Hessian normal form (8) where is again the dot product, is a vector describing the surface normal to a plane, is a vector representing all points on the plane, and d is a scalar. We need to check whether the constraint planes are parallel or coincident, that is if (9) before we can determine their intersection. The equation for the intersection of the two constraint planes is a line here written as (10) where u is a free parameter. Taking the dot product of the above with each plane normal gives two equations with unknown scalars cL and cR. (11) Solving the two equations for cL and cR gives (12) Inserting cL and cR in (10) determines the intersection of constraints or constraint line p. In analogy to the 2D aperture problem and the intersection of constraints we can now define two plausible strategies for solving the 3D aperture problem: The shortest distance in 3-D (velocity) space between the starting point of the stimulus line and the constraint line is the line or vector normal through point. In order to determine the intersection point of the vector normal with the constraint line we pick two arbitrary points and on intersection constraint line by choosing a scalar u (e. g. , 0. 5). (13) Together with point we can compute scalar as (14) which determines the closest intersection point on the constraint line: (15) We can define a cyclopean constraint line in terms of the cyclopean origin and projection point on a fronto-parallel screen where and are the averages of the 2D normal co-ordinates for the left and right eye projections. If we measure disparity at the same retinal coordinates as the horizontal offset between the left and right eye anchored at position then we can define new points b with and d with. (Alternatively, we may establish an epipolar or more sophisticated disparity constraint.) The resulting two points together with the corresponding nodal points a and c define two constraint lines as in (2), one for the left and the other for the right eye. By inserting the new co-ordinates from above into (4) it is easy to see that condition (6) holds and the scalar for the intersection of lines can be found as in (3). The intersection in cartesian co-ordinates can be transformed into spherical co-ordinates using vectors and to determine azimuth α in the horizontal plane (16) Similarly, for base vectors and elevation β is given by (17) Speed in 3D space is equivalent to the norm of vector s written as.
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.
computer science/natural and synthetic vision
physiology/sensory systems
neuroscience/sensory systems
computational biology/computational neuroscience
neuroscience/psychology
biotechnology/bioengineering
mathematics/statistics
On the Inverse Problem of Binocular 3D Motion Perception