Enhancing Rotation Averaging and Global Positioning by an Adaptive Robust Kernel

Abstract. Motion averaging (MA) offers an efficient and mostly linear means for estimating image sets pose and provides a reliable initialization for large-scale structure-from-motion (SfM) pipelines. Nonetheless, MA, which comprises rotation and translation averaging (RA & TA, respectively), can be severely affected by the presence of outliers that can greatly degrade the performance of the entire optimization process or even lead to the divergence of the SfM solution. While robust loss functions have been applied to mitigate outliers effect, their performance depends heavily on the choice of parameters and requires manual tuning and prior knowledge of the residual distribution. To make MA more robust, we enhance it by incorporating an adaptive robust kernel that automatically adjusts its parameter to the residual distribution. This adaptive behavior balances robustness and sensitivity, removing the need for manual parameter tuning. In addition, to address the ill-posed nature of TA, we adopt a global positioning framework that jointly estimates the camera and 3D point positions. Experimental results show how the adaptive robust kernel consistently outperforms state-of-the-art fixed-parameter functions. It improves accuracy in both RA and global positioning, particularly in scenes with high levels of noise or outliers. These results demonstrate the effectiveness of adaptive robust kernels for improving the reliability and generalization of MA pipelines in challenging reconstruction scenarios.