Authors:
Zhuohao Liu
1
;
Changyu Diao
2
;
Wei Xing
1
and
Dongming Lu
3
Affiliations:
1
College of Computer Science and Technology, Zhejiang University, Hangzhou and China
;
2
Cultural Heritage Institute, Zhejiang University, Hangzhou, China, Key Scientific Research Base for Digital Conservation of Cave Temples, Zhejiang University, State Administration for Cultural Heritage and China
;
3
College of Computer Science and Technology, Zhejiang University, Hangzhou, China, Key Scientific Research Base for Digital Conservation of Cave Temples, Zhejiang University, State Administration for Cultural Heritage and China
Keyword(s):
Structure from Motion, Distributed Bundle Adjustment, Consensus, Block Partitioning, Biclustering.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Software Engineering
;
Stereo Vision and Structure from Motion
Abstract:
We present a critical parameter consensus framework to improve the efficiency of Distributed Bundle Adjustment (DBA). Existing DBA methods are based solely on either camera consensus or point consensus, often resulting in excessive local computation time or large data transmission overhead. To address this issue, we jointly partition points and cameras, and perform the consensus on both overlapping cameras and points. Our joint block partitioning method first initializes a non-overlapping block partition, maximizing local problem constraints and ensuring a uniform partition. Then overlapping cameras and points are added in a greedy manner to maximize the partition score that quantifies the efficiency of DBA for local blocks. Experimental results on public datasets show that we can achieve better computational efficiency without loss of accuracy.