We address the problem of recovering high-level, volumetric and segmented (or part-based) descriptions of objects from intensity images. As input we use three closely spaced images of an object and recover descriptions based on generalized cylinders (GCs). We start by extracting a hierarchy of groups from contour images in the three views. Grouping is based on proximity, parallelism, and symmetry. The groups in the three views are matched and their contours are labeled as "true" edges. We then infer the GC axis, its cross-section, and the scaling function. 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 coarse volumetric descriptions obtained are refined to include surface details as seen in the intensity images. We demonstrate results on real images of moderately complex objects with texture and shadows.

Synthesizing the image of a 3‐D scene as it would be captured by a camera from an arbitrary viewpoint is a central problem in Computer Graphics. Given a complete 3‐D model, it is possible to render the scene from any viewpoint. The construction of models is a tedious task. Here, we propose to bypass the model construction phase altogether, and to generate images of a 3‐D scene from any novel viewpoint from prestored images. Unlike methods presented so far, we propose to completely avoid inferring and reasoning in 3‐D by using projective invariants. These invariants are derived from corresponding points in the prestored images. The correspondences between features are established off‐line in a semi‐automated way. It is then possible to generate wireframe animation in real time on a standard computing platform. Well understood texture mapping methods can be applied to the wireframes to realistically render new images from the prestored ones. The method proposed here should allow the integration of computer generated and real imagery for applications such as walkthroughs in realistic virtual environments. We illustrate our approach on synthetic and real indoor and outdoor 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.

We address the recovery of segmented, 3-D descriptions of an object from intensity images. We use three views of an object from slightly different viewpoints as our input. For each image we extract a hierarchy of groups based on proximity, parallelism and symmetry in a robust manner. The groups in the three images are matched by computing the epipolar geometry. For each set of matched groups from the three images, we then label the contours of the groups as "true" or "limb" edges. Using the information about groups, the label associated with their contours and projective properties of subclasses of Generalized Cylinders, we infer the 3-D structure of these groups. The proposed method not only allows robust shape recovery but also produces segmented parts. Our approach can also deal with groups generated as a result of texture or shadows on the object. We present results on real images of moderately complex objects.>