$f$-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception

Overview

f-Cal calibrates aleatoric uncertainty by constraining output distributions across multiple examples through f-divergence minimization. Object-detection and monocular-depth benchmarks evaluate whether predicted uncertainty better reflects underlying errors than existing approaches.