2021/11/16 by Seong Hun Lee, Javier Civera, Lee, Seong Hun +1 · 1 citation
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Structural Health Monitoring Techniques #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2111.08831
openalex publication_date 2021/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a novel hierarchical approach for multiple rotation averaging, dubbed HARA. Our method incrementally initializes the rotation graph based on a hierarchy of triplet support. The key idea is to build a spanning tree by prioritizing the edges with many strong triplet supports and gradually adding those with weaker and fewer supports. This reduces the risk of adding outliers in the spanning tree. As a result, we obtain a robust initial solution that enables us to filter outliers prior to nonlinear optimization. With minimal modification, our approach can also integrate the knowledge of the number of valid 2D-2D correspondences. We perform extensive evaluations on both synthetic and real datasets, demonstrating state-of-the-art results.