Global registration of large collections of range images with an improved Optimization-on-a-Manifold approach
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Global registration,3D scanning,Range images,Correspondence selection,Newton-type optimization,Differential geometry
论文评审过程：Received 29 June 2013, Revised 29 December 2013, Accepted 25 February 2014, Available online 20 March 2014.
论文官网地址：https://doi.org/10.1016/j.imavis.2014.02.012
