  A novel shape descriptor for cluttered scenes is presented, the Radial
Intersection Count Image (RICI), and is shown to significantly outperform the
classic Spin Image (SI) and 3D Shape Context (3DSC) in both uncluttered and,
more significantly, cluttered scenes. It is also faster to compute and compare.
The clutter resistance of the RICI is mainly due to the design of a novel
distance function, capable of disregarding clutter to a great extent. As
opposed to the SI and 3DSC, which both count point samples, the RICI uses
intersection counts with the mesh surface, and is therefore noise-free. For
efficient RICI construction, novel algorithms of general interest were
developed. These include an efficient circle-triangle intersection algorithm
and an algorithm for projecting a point into SI-like ($\alpha$, $\beta$)
coordinates. The 'clutterbox experiment' is also introduced as a better way of
evaluating descriptors' response to clutter. The SI, 3DSC, and RICI are
evaluated in this framework and the advantage of the RICI is clearly
demonstrated.
