Abstract

Many methods for 3D reconstruction in computer vision rely on probability models, for example, Bayesian reasoning. Here we intro- duce a probability model of surface visibilities in densely cluttered 3D scenes. The scenes consist of a large number of small surfaces distributed randomly in a 3D view volume. An example is the leaves or branches on a tree. We derive probabilities for surface visibility, instantaneous image velocity under egomotion, and binocular half–occlusions in these scenes. The probabilities depend on parameters such as scene depth, ob ject size, 3D density, observer speed, and binocular baseline. We verify the cor- rectness of our models using computer graphics simulations, and briefly discuss applications of the model to stereo and motion.