Pair-Wise Range Image Registration: A Study in Outlier Classification

Abstract

In this paper, we present a robustness study on several popular techniques for performing fine registration of partially overlapping 2.5D range image pairs, with a focus on model building. In our first set of tests, we qualitatively evaluate the output of several iterative closest point (ICP) variants on real-world data. Our second set of tests expands to include additional ICP variants and an implementation of Chen and Medioni's point-to-plane minimizing algorithm. These tests evaluate quantitatively how well these algorithm variants are able to correct initial simulated rigid rotation and translation errors. The aim of these variants in both sets of tests is to classify as outliers particular point pairs containing vertices outside of the region of overlap of the two range images. In addition to testing these variants with different parameter settings, we also study how performing topologically uniform subsampling of the meshes affects the registration quality.