Volumetric, 3D, part-based descriptions of complex objects in a scene can be highly beneficial for many tasks such as generic object recognition, navigation, and manipulation. However, it has been difficult to derive such descriptions from image data. There has been some progress in getting such descriptions from range data or from perfect contours, but analysis of a real intensity image presents many difficulties. The object and part boundaries do not completely correspond to image boundaries. The detected boundaries are often fragmented and many boundaries due to surface markings, shadows, and noise are present. In addition, inference of 3D from a 2D image is difficult. The paper describes a method to compute the desired descriptions from a single image by exploiting projective properties of a class of generalized cylinders and of possible joints between them. Experimental results on some examples are given.

We address the recovery of object-level 3D descriptions of some classes of curved-axis generalized cylinders. For this, the first part of the paper analyzes the projective properties of two common generic shapes, planar right constant generalized cylinders (PRCGCs) and circular planar right generalized cylinders (circular PRGCs). The properties we analyze include new geometric invariant and quasi-invariant properties of the orthographic projection of the above shapes and a useful classification of their structural properties as functions of their pose. The second part of the paper describes an implemented system which detects and recovers PRCGCs and circular PRGCs from an intensity image in the presence of noise, surface markings, shadows, and partial occlusion. The methods exploit the projective properties to hypothesize and verify relevant curved-axis objects, thus explicitly using the three-dimensionality of the objects and of the desired descriptions. This work extends past work on the recovery of volumetric shapes from an intensity image by addressing new primitives, deriving new properties and by developing a system that recovers them from an intensity image. We demonstrate our method on several real intensity images.

Addresses the inference of 3-D segmented descriptions of complex objects from a single intensity image. The authors' approach is based on the analysis of the projective properties of a small number of generalized cylinder primitives and their relationships in the image which make up common man-made objects. Past work on this problem has either assumed perfect contours as input or used 2-dimensional shape primitives without relating them to 3-D shape. The method the authors present explicitly uses the 3-dimensionality of the desired descriptions and directly addresses the segmentation problem in the presence of contour breaks, markings shadows and occlusion. This work has many significant applications including recognition of complex curved objects from a single real intensity image. The authors demonstrate their method on real images.

Addresses the problem of segmentation and recovery of 3-D object-centered descriptions of two large sub-classes of curved axis generalized cylinders, PRCGCs and circular PRGCs, from a single real intensity image. The purpose of this work is to augment the set of 3-D primitives which can be recovered, beyond previous work which has addressed mainly straight axis ones, so that more complex objects can be handled. The authors' approach is based on the exploitation of geometric projective properties as well as structural properties of the contours of circular PRGCs. The implemented method works in the presence of noise, contour breaks, markings, shadows and occlusion. The authors demonstrate their method on real images.

The geometric protective properties of the contours of right generalized cylinders with a planar, but not necessarily straight, axis and circular, but possibly varying in size, cross-sections (called circular PRGCs) are addressed. Important rigourous quasi-invariant properties of circular PRGCs and invariant properties for their subclasses are derived. Their application for 2-D descriptions and for recovery of complete 3-D object-centered descriptions from the 2-D contours is shown.>