Critical Parameter Consensus For Efficient Distributed Bundle Adjustment
PROCEEDINGS OF THE 14TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISAPP), VOL 5(2019)
摘要
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.
更多查看译文
关键词
Structure from Motion, Distributed Bundle Adjustment, Consensus, Block Partitioning, Biclustering
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络