顾及场景连通性的混合式SfM方法

A hybrid SfM method considering scene connectivity

SfM方法在三维稀疏重建方面获得了巨大的成功,但面对大规模场景重建问题时,该方法仍面临着严重的挑战。针对现有混合式SfM方法场景划分影像分布松散、子簇扩展效率低,以及子簇合并稳健性差等问题,本文提出一种顾及场景连通性的混合式SfM方法。首先,提出一种基于归一化割的多因子联合场景划分算法,有效地解决了场景划分后子簇内影像空间分布松散的问题;其次,提出一种顾及分区连通性的子簇均衡扩展算法,提高了扩展的效率及子簇间的连通程度;然后,通过在局部重建阶段引入质检及二次重建机制,消除了局部重建质量不合格子簇对合并的影响,并提出一种顾及簇间连通性的子簇合并算法,实现子簇间的稳健合并;最后,利用多组公开数据集和多视倾斜摄影数据集进行试验验证。结果表明:本文方法在稳健性和效率等方面均优于目前先进的方法,具有较好的可行性和先进性。

SfM method has achieved great success in 3D sparse reconstruction,but it still meets serious challenges in large-scale scene reconstruction.Aiming at solving the problem of loose image distribution,low efficiency of subcluster expansion and weak robustness of subcluster merging in existing hybrid SfM methods,a hybrid SfM method considering scene connectivity isproposed in this paper.Firstly,a multifactor joint scene division algorithm based on normalized cut is proposed,which effectively solves the problem of loose image space distribution in subclusters after scene division;Secondly,a subcluster balanced expansion algorithm considering partition connectivity is proposed to improve the expansion efficiency and connectivity between subclusters;Then,a quality check and secondary reconstruction mechanism in the local reconstruction stage are introduced to eliminate the influence of subclusters with unqualified local reconstruction quality on merging,and a subcluster merging algorithm considering the connectivity between clusters is proposed to implement the robust merging among subclusters.Finally,experimental validation is conducted using multiple open datasets and multi-view datasets,and the results show that the method proposed in this paper is superior to state-of-the-art methods in terms of robustness and efficiency,and has better feasibility and advancement.