The authors describe techniques that generate multiple interpretations from dense range images of piles of unknown objects and methods that use physical law, such as object stability, to rank the interpretations. Each of the interpretations completely accounts for the observed range data, but the interpretations differ in the ways visible portions of objects are extended into the occluded portions of the scene. Experiments with 100 range images indicate that the techniques are fairly robust when the scenes consist of shapes that are approximately prismatic or cylindrical. These techniques are based on novel approaches in several key areas, including explicit use of sensor geometry, generic shape models to synthesize scene descriptions, spatial-reasoning techniques that incorporate knowledge about the laws of physics, direct estimation of the physical properties of the objects in the scene, and detection and refinement of descriptions of approximately planar or cylindrical surfaces.>

Techniques for calculating the three-dimensional region of acceptable locations for vision sensors and light sources are discussed. An integrated method to position both a camera and a light source based on the requirements of a given vision task is described. The method also selects the appropriate lens aperture. The proposed techniques require only a small amount of computation (approximately 1 minute of elapsed time on a time-shared VAX 8600). The proposed approach is illustrated with an example object and experimental results are described for that object.>

A system that automatically generates the possible camera locations for observing an object is described. The approach, which uses models of the object and the camera, is based on meeting the requirements that the spatial resolution be above a minimum value, all surface points be in focus, all surfaces lie within the sensor field-of-view, and no surface points be occluded. Each sensing requirement is converted into a geometric constraint on the sensor location, from which the three-dimensional region of viewpoints that satisfies that constraint is computed. The intersection of these regions is the space where a sensor may be located.>

The problem of automatically generating the possible camera locations for observing an object is defined, and an approach to its solution is presented. The approach, which uses models of the object and the camera, is based on meeting the requirements that: the spatial resolution be above a minimum value, all surface points be in focus, all surfaces lie within the sensor field of view and no surface points be occluded. The approach converts each sensing requirement into a geometric constraint on the sensor location, from which the three-dimensional region of viewpoints that satisfies that constraint is computed. The intersection of these regions is the space where a sensor may be located. The extension of this approach to laser-scanner range sensors is also described. Examples illustrate the resolution, focus, and field-of-view constraints for two vision tasks.>

A technique for verifying an object hypothesis by comparing hypothesized edge patterns to detected edge patterns is presented. Positive evidence for a hypothesis is identified as those portions of the model edges that are found in the image, either as geometrical features (such as straight lines or circular arcs} or as linked edges of arbitrary shape. Occluded portions of a model edge are detected by analyzing junctions of image and model edge chains. Negative evidence is identified as those portions of the model edges that are not found and are not occluded. We describe the types of junctions that can occur between image and model edges and discuss a technique for identifying occluded regions based on these junctions. Preliminary results of these techniques are also presented.