Toward True 3 D Object Recognition

This paper addresses the problem of recognizing three-dimensional (3D) objects in photographs and image sequences. It revisits viewpoint invariants as a local representation of shape and appearance, and proposes a unified framework for object recognition where object models consist of a collection of small (planar) patches, their invariants, and a description of their 3D spatial relationship. This approach is applied to two fundamental instances of the 3D object recognition problem: (1) modeling rigid 3D objects from a small set of unregistered pictures and recognizing them in cluttered photographs taken from unconstrained viewpoints, and (2) recognizing non-uniform texture patterns despite appearance variations due to non-rigid transformations and changes in viewpoint. It is validated through several experiments, and extensions to the analysis of video sequences and the recognition of object categories are briefly discussed.

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