Radial Intersection Count Image: a Clutter Resistant 3D Shape Descriptor

TL;DR

The paper introduces the Radial Intersection Count Image (RICI), a new 3D shape descriptor that is more clutter resistant, faster, and noise-free compared to existing methods, with novel algorithms and evaluation techniques.

Contribution

It presents the RICI descriptor, novel algorithms for its construction, and a new clutterbox evaluation framework, advancing 3D shape matching in cluttered scenes.

Findings

RICI outperforms Spin Image and 3D Shape Context in cluttered scenes.

RICI is faster to compute and compare.

The clutterbox experiment effectively evaluates descriptor robustness.

Abstract

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.