Inference of segmented, volumetric shape from three intensity images

We present a method to infer segmented and full volumetric descriptions of objects from intensity images. We use three weakly calibrated images from closely spaced viewpoints as input. Deriving full volumetric descriptions requires the development of robust inference rules. The inference rules are based on local properties of generalized cylinders (GCs). We first detect groups in each image based on proximity, parallelism and symmetry. The groups in the three images are matched and their contours are labelled as “true” and “limb” edges. We use the information about groups and the label associated with their contours to recover visible surfaces and their surface axes. To extract the complete volume in terms of a GC, we need to infer the GC axis, its cross section and the scaling function. The properties of straight and curved axis generalized cylinders are used locally on the visible surfaces to obtain the GC axis. The cross section is recovered if seen in the images, else it is inferred using the visible surfaces and GC properties. We consider groups with true edges, limb edges or a combination of both. The final descriptions are volumetric and in terms of parts. Sometimes, when not enough information is present to make volumetric inferences, the descriptions remain at the surface level. We demonstrate results on real images of moderately complex objects with texture and shadows