Pose-Invariant 3D Object Recognition Using Linear Combination of 2D Views and Evolutionary Optimisation

In this work, we present a method for model-based recognition of 3d objects from a small number of 2d intensity images taken from nearby, but otherwise arbitrary viewpoints. Our method works by linearly combining images from two (or more) viewpoints of a 3d object to synthesise novel views of the object. The object is recognised in a target image by matching to such a synthesised, novel view. All that is required is the recovery of the linear combination parameters, and since we are working directly with pixel intensities, we suggest searching the parameter space using an evolutionary optimisation algorithm in order to efficiently recover the optimal parameters and thus recognise the object in the scene