Level of Detail (LoD) techniques for real-time... In this paper, we are particularly interested in the problem of realistic collision simulation in scenes where large numbers of objects are colliding and processing must occur in real-time. An interruptible and therefore degradable collision handling mechanism is used and the perceptual impact of this degradation is explored. We look for ways in which we can optimise the realism of such simulations and describe a series of psychophysical experiments that investigated different factors affecting collision perception, including eccentricity, separation, distractors, causality and accuracy of physical response. Finally, strategies for incorporating these factors into a perceptually adaptive real-time simulation of large numbers of visually similar objects are presented.

For many systems that produce physically based animations, plausibility rather than accuracy is acceptable. We consider the problem of evaluating the visual quality of animations in which physical parameters have been distorted or degraded, either unavoidably due to real-time frame-rate requirements, or intentionally for aesthetic reasons. To date, no generic means of evaluating or predicting the fidelity, either physical or visual, of the dynamic events occurring in an animation exists. As a first step towards providing such a metric, we present a set of psychophysical experiments that established some thresholds for human sensitivity to dynamic anomalies, including angular, momentum and spatio-temporal distortions applied to simple animations depicting the elastic collision of two rigid objects. In addition to finding significant acceptance thresholds for these distortions under varying conditions, we identified some interesting biases that indicate non-symmetric responses to these distortions (e.g., expansion of the angle between postcollision trajectories was preferred to contraction and increases in velocity were preferred to decreases). Based on these results, we derived a set of probability functions that can be used to evaluate the visual fidelity of a physically based simulation. To illustrate how our results could be used, two simple case studies of simulation levels of detail and constrained dynamics are presented.

Complex dynamic events are often misunderstood at the perceptual and intuitive level. In spite of these difficulties, experts, such as athletes or billiard players, learn to make very precise dynamic judgments. This study explored the basis of such judgmental abilities using the example of rotational motions (spin) as they occur in billiards. Five experiments were carried out to evaluate two paradigms that might explain how observers may judge the dynamics of spin: perceptual attunement and learning of procedural heuristics that apply to one salient dimension of information. Conceptual knowledge about the effects of spin on trajectories of billiard balls was found to be erroneous. Almost all novices and more than half of the experts tested had mistaken beliefs about one particular type of spin (english). These beliefs were mirrored in animated contexts where subjects had to observe computer-generated events of spinning balls that had possible and impossible effects. Visual performance ...

We prove the following: Given four (or more) orthographic views of three points then (a) the views almost surely have no rigid interpretation but (b) if they do then they almost surely have at most thirty-two rigid interpretations. Part (a) means that the measure of “false targets”, viz., the measure of nonrigid motions that project to views having rigid interpretations, is zero. Part (b) means that rigid interpretations, when they exist, are not unique. Uniqueness of interpretation can be obtained if a point is added, but not if views are added. Our proof relies on an upper semicontinuity theorem for proper mappings of complex algebraic varieties. We note some psychophysical motivations of the theory. 1 Bennett & Hoffman Rigid 3D Structure

In this chapter, we will discuss the usefulness of eye-tracking for computer applications that attempt to render simulations at interactive rates.

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Many human activities require precise judgments about the physical properties and dynamics of multiple objects. Classic work suggests that people’s intuitive models of physics are relatively poor and error-prone, based on highly simplified heuristics that apply only in special cases or incorrect general principles (e.g., impetus instead of momentum). These conclusions seem at odds with the breadth and sophistication of naive physical reasoning in real-world situations. Our work measures the boundaries of people’s physical reasoning and tests the richness of intuitive physics knowledge in more complex scenes. We asked participants to make quantitative judgments about stability and other physical properties of virtual 3D towers. We found their judgments correlated highly with a model observer that uses simulations based on realistic physical dynamics and sampling-based approximate probabilistic inference to efficiently and accurately estimate these properties. Several alternative heuristic accounts provide substantially worse fits. Keywords: intuitive physics, dynamics, perception, model

The behaviour of objects in the physical world is described by Newtonian mechanics, using dynamic concepts such as force and mass. However, it has been reported that many people have intuitive preconceptions concerning mechanical events that, although incorrect according to Newtonian mechanics, are highly stable and widespread. 3 Profitt and Gilden showed that people use only one dimension of information when making dynamical judgements. 6 Therefore, when a dynamic event involves more than one dimension of information such as velocity and rotation (i.e. an extended body motion as opposed to a particle which has only one dimension of information), humans are less able to correctly identify anomalous physical behaviour. They also discovered that judgements about collisions were made based on heuristics and that people are influenced by kinematic data, such as velocity after impact and the way that the colliding objects ricochet. 4