Fast Global Registration
We present an algorithm for fast global registration of partially overlapping 3D surfaces. The algorithm operates on candidate matches that cover the surfaces. A single objective is optimized to align the surfaces and disable false matches. The objective
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Abstract. We present an algorithm for fast global registration of partially overlapping 3D surfaces. The algorithm operates on candidate matches that cover the surfaces. A single objective is optimized to align the surfaces and disable false matches. The objective is defined densely over the surfaces and the optimization achieves tight alignment with no initialization. No correspondence updates or closest-point queries are performed in the inner loop. An extension of the algorithm can perform joint global registration of many partially overlapping surfaces. Extensive experiments demonstrate that the presented approach matches or exceeds the accuracy of state-of-the-art global registration pipelines, while being at least an order of magnitude faster. Remarkably, the presented approach is also faster than local refinement algorithms such as ICP. It provides the accuracy achieved by well-initialized local refinement algorithms, without requiring an initialization and at lower computational cost.
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Introduction
Registration of three-dimensional surfaces is a central problem in computer vision, computer graphics, and robotics. The problem is particularly challenging when the surfaces only partially overlap and no initial alignment is given. This difficult form of the problem is encountered in scene reconstruction [7,39], 3D object retrieval [15,29], camera relocalization [13], and other applications. In order to deal with noisy data and partial overlap, practical registration pipelines employ iterative model fitting frameworks such as RANSAC [31]. Each iteration samples a set of candidate correspondences, produces an alignment based on these correspondences, and evaluates this alignment. If a satisfactory alignment is found, it is refined by a local registration algorithm such as ICP [30]. The combination of sampling-based coarse alignment and iterative local refinement is common in practice and is designed to produce a tight registration even with challenging input [7,19,39]. While such registration pipelines are common, they have significant drawbacks. Both the model fitting and the local refinement stages are iterative and perform computationally expensive nearest-neighbor queries in their inner loops. Much of the computational effort is expended on testing candidate alignments that are subsequently discarded. And the inelegant decomposition into a global alignment stage and a local refinement stage is itself a consequence of the low precision of global alignment frameworks. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part II, LNCS 9906, pp. 766–782, 2016. DOI: 10.1007/978-3-319-46475-6 47
Fast Global Registration
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In this paper, we present a fast global registration algorithm that does not involve iterative sampling, model fitting, or local refinement. The algorithm does not require initialization and can align noisy partially overlapping surfaces. It optimizes a robust objective defined densely over the surfaces. Due to this dense coverage, the algorithm directly produces an alignment
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