  The problem of 3D object recognition is of immense practical importance, with
the last decade witnessing a number of breakthroughs in the state of the art.
Most of the previous work has focused on the matching of textured objects using
local appearance descriptors extracted around salient image points. The
recently proposed bag of boundaries method was the first to address directly
the problem of matching smooth objects using boundary features. However, no
previous work has attempted to achieve a holistic treatment of the problem by
jointly using textural and shape features which is what we describe herein. Due
to the complementarity of the two modalities, we fuse the corresponding
matching scores and learn their relative weighting in a data specific manner by
optimizing discriminative performance on synthetically distorted data. For the
textural description of an object we adopt a representation in the form of a
histogram of SIFT based visual words. Similarly the apparent shape of an object
is represented by a histogram of discretized features capturing local shape. On
a large public database of a diverse set of objects, the proposed method is
shown to outperform significantly both purely textural and purely shape based
approaches for matching across viewpoint variation.

Computer Vision and Pattern Recognition