Incremental Translation Averaging

Translation averaging is known to be more difficult than rotation averaging due to scale ambiguity, estimation sensitivity, and solution uncertainty. Existing approaches have exposed their limitations in terms of accuracy, robustness, simplicity, or efficiency. To tackle this tough problem, a simple yet effective translation averaging pipeline, termed as Incremental Translation Averaging (ITA), is proposed in this paper. It combines the advantages of high accuracy and robustness in incremental parameter estimation pipeline and the advantages of high simplicity and efficiency in global motion averaging approach. Unlike the traditional translation averaging methods which estimate all the absolute camera locations simultaneously and suffer from inaccuracy in parameter estimation and incompleteness in scene reconstruction, our ITA computes them novelly in an incremental way with higher accuracy and robustness. Thanks to the introduction of incremental parameter estimation thought into the translation averaging pipeline, 1) our ITA is robust to measurement outliers and accurate in parameter estimation; and 2) our ITA is simple and efficient because of its less dependency on complicated optimization, carefully-designed preprocessing, or additional information. Comprehensive evaluations on the 1DSfM dataset demonstrate the effectiveness of our ITA and its advantages over several state-of-the-art translation averaging approaches.

Automated summary

This paper proposes a simple yet effective translation averaging pipeline, Incremental Translation Averaging (ITA), which overcomes limitations in accuracy, robustness, simplicity, and efficiency present in traditional translation averaging methods. ITA computes camera locations incrementally, leading to higher accuracy and robustness, and is robust to measurement outliers and accurate in parameter estimation while being simple and efficient. Comprehensive evaluations on the 1DSfM dataset demonstrate the effectiveness of ITA and its superiority over state-of-the-art translation averaging approaches.