Global registration of large collections of range images with an improved Optimization-on-a-Manifold approach

• Goal: new approach to enable global registration of large collections of point sets.
• We consider an optimization-on-a-manifold for global registration of multiple scans.
• We evidence computational and convergence issues in the original approach.
• We propose computationally effective correspondence update and other improvements.
• Results: better accuracy compared to state-of-the-art, good computational performance.

Concurrently obtaining an accurate, robust and fast global registration of multiple 3D scans is still an open issue for modern 3D modeling pipelines, especially when high metric precision as well as easy usage of high-end devices (structured-light or laser scanners) are required. Various solutions have been proposed (either heuristic, iterative and/or closed form solutions) which present some compromise concerning the fulfillment of the above contrasting requirements. Our purpose here, compared to existing reference solutions, is to go a step further in this perspective by presenting a new technique able to provide improved alignment performance, even on large datasets (both in terms of number of views and/or point density) of range images. Relying on the ‘Optimization-on-a-Manifold’ (OOM) approach, originally proposed by Krishnan et al., we propose a set of methodological and computational upgrades that produce an operative impact on both accuracy, robustness and computational performance compared to the original solution. In particular, always basing on an unconstrained error minimization over the manifold of rotations, instead of relying on a static set of point correspondences, our algorithm updates the optimization iterations with a dynamically modified set of correspondences in a computationally effective way, leading to substantial improvements in terms of registration accuracy and convergence trend. Other proposed improvements are directed to a substantial reduction of the computational load without sacrificing the alignment performance. Stress tests with increasing view misalignment allowed us to appreciate the convergence robustness of the proposed solution. Eventually, we demonstrate that for very large datasets a further computational speedup can be reached by the adoption of a hybrid (local heuristic followed by global optimization) registration approach.

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