Automatic 3D model acquisition and 3D tracking of simple objects under motion using a single camera is often difficult due to the sparsity of information from which to establish the model. We developed an automatic scheme that first computes a simple Euclidean model of the object and then enriches this model using hyperpatches. These hyperpatches contain information on both the orientation and intensity pattern variation of roughly planar patches on an object. This information allows both the spatial and intensity distortions of the projected patch to be modeled accurately under 3D object motion. Considering human tracking as a specific application, we show that hyperpatches can not only be computed automatically during model acquisition from a monocular image sequence, but that they are also extremely appropriate for the task of visual tracking.

This paper is about automatically reconstructing the full 3D surface of an object observed in motion by a single static camera. We introduce the geotensity constraint that governs the relationship between four images of a moving object under fairly general lighting conditions. We show that it is possible in theory to solve for 3D surface structure for both the case of a single point light source and a pair of point, light sources and propose that a solution exists for an arbitrary number point light sources. The surface may or may not be textured. We then give an example of automatic surface reconstruction of a face under a point light source. The geotensity constraint provides the theoretical foundation for the full automatic 3D reconstruction of Lambertian objects using a single fixed camera, and arbitrary unknown object motion under arbitrary lighting conditions.

Automatic 3D model acquisition and 3D tracking of simple objects under motion using a single camera is often difficult due to the sparsity of information from which to establish the model. We have developed an automatic scheme that first computes a simple pointalistic Euclidean model of the object and then enriches this model using hyper-patches. These hyper-patches contain information on both the orientation and intensity pattern variation of roughly planar patches on an object. This information allows both the spatial and intensity distortions of the projected patch to be modelled accurately under 3D object motion. We show that hyper-patches not only can be computed automatically during model acquisition from a monocular image sequence, but that they are also extremely appropriate for the task of visual tracking.

We describe a number of advances in the analysis of road scenes when the scene contains multiple moving objects and is observed by a single nonsteerable camera mounted on the front of a vehicle. Our structure from motion approach to scene segmentation derives front the observed motions of independently moving objects and requires no prior knowledge. We describe a hierarchy of camera models for the analysis of the scene, the simpler models handle degeneracies that occur in the more complex models. The major technical contribution is the recursive computation of feature clusters, which are fed forward over time. This closed loop feature tracking generates extended feature trajectories which significantly improve the discriminating power of scene segmentation.>