Abstract. 3D registration is an essential step in 3D reconstruction of real objects. In this paper, the fine registration methods for point clouds are classified systematically and analyzed theoretically. For several typical fine registration algorithms, experimental comparisons are also carried out on the effect of sampling parameters, iterations, iterative threshold and random noise. Furthermore, the stability and efficiency of the algorithms are tested. Based on the experimental results, the problems in current registration methods and the prospects of future research directions are also presented.
This article studies the problem of generating a piano score following video from an audio recording in a fully automated manner. This problem contains two components: identifying the piece and aligning the audio with raw sheet music images. Unlike previous work, we focus primarily on working with raw, unprocessed sheet music from IMSLP, which may contain filler pages, other unrelated pieces or movements, or repeats and jumps whose locations are unknown a priori. To solve this problem, we combine state-ofthe-art methods with a novel alignment algorithm called Hierarchical DTW to handle discontinuities, which are the bottleneck on system performance. Hierarchical DTW handles repeats and jumps by considering all line breaks as possible jump locations, and it applies DTW at both a feature level and a segment level. We evaluate our algorithm with 200 PDFs from IMSLP and real audio recordings from Youtube. Our experiments show that Hierarchical DTW consistently outperforms a previously proposed Jump DTW algorithm in handling various types of discontinuities. We present extensive experimental results and analysis of the proposed algorithm.
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