Abstract.
Local appearance models in the neighborhood of salient im- age features, together with local and/or global geometric constraints, serve as the basis for several recent and effective approaches to 3D ob- ject recognition from photographs. However, these techniques typically either fail to explicitly account for the strong geometric constraints asso- ciated with multiple images of the same 3D ob ject, or require a large set of training images with much overlap to construct relatively sparse ob- ject models. This paper proposes a simple new method for automatically constructing 3D ob ject models consisting of dense assemblies of small surface patches and affine-invariant descriptions of the corresponding texture patterns from a few (7 to 12) stereo pairs. Similar constraints are used to effectively identify instances of these models in highly cluttered photographs taken from arbitrary and unknown viewpoints. Experiments with a dataset consisting of 80 test images of 9 ob jects, including com- parisons with a number of baseline algorithms, demonstrate the promise of the proposed approach.