Automatic multiview quadruple alignment of unordered range scans

This paper presents a new multiview alignment algo-rithm that performs both the coarse and fine alignment of unordered sets of range scans. Our algorithm selects quadruples of range scans, which have feature points of their 2D projections in common. These quadruples are then verified using an Iterative Closest Point (ICP) algorithm. The accepted quadruples form incomplete models of an ob-ject that can be aligned using isometries of the Principal Component Analysis (PCA) in combination with an ICP al-gorithm. The output of our method is a set of finely aligned meshes. Our method was applied to range scans of different clusters of objects varying in the type of acquisition system, the number of range scans, the scan resolution, and scan ac-curacy. Results show that our method is both effective and efficient for the alignment of meshes: it is capable of align-ing object meshes with various properties, aligns the ma-jority of meshes without a priori knowledge, and doesn’t re-quire a multiview ICP algorithm to improve the final align-ment